Abstract

eutonomous vehiles nvigtion implies tht deisions re tken ontinuously sed on prtil nd unertin knowledge of their environmentF his is onstrined y limited digitl represenE ttions of the environment nd unertinty ssoited with the pereption proessF purtherD it is di0ult to predit the ehviour of the pereived entitiesF his is highlighted t rossrod intersetionsD where most rod idents ourF his thesis proposes deisionEmking proess tht resons with di'erent types of unertinties inluding the ehviour of the oserved drivers to pln the vehile motionF o understnd the ontext nd the ehviours of the oserved driver9s ehviourD mhine lerning pproh is proposedF he result is used y the deisionEmking proess to uild proE ilisti estimtion of the environmentF he vehile motion is plnned tking into ount the e'et this might hve when the vehile interts with other entitiesF yur pproh rewrds E tions tht promote intertion nd redues riskF he system ehviour is nlysed y using set of metris derived from the senrio nlysis s well s sfety nd opertionl onstrintsF o infer rod ontextD qussin roesses re pplied to lern motion ptterns from simulted trjetoriesD whih inluded the e'et of vehile intertions with other entitiesF he resulting ptterns re segmented into res nd used to understnd the ehviour of vehile pprohing n intersetionF henD ndom porest glssi(ers re pplied to estimte the driver mnoeuvre in eh reF he dtset used for this trining is uilt using dt reorded from rod trils nd simultionsF hese lssi(ers infer lterl nd longitudinl mnoeuvre y extrting fetures from the vehile trjetoriesF his pproh shows tht the rod ontext improves the mnoeuvre lssi(tion nd tht y mixing few rel nd simulted trjetoriesD it is possile to lssify the mnoeuvres of drivers rriving t n intersetionF he deisionEmking proess is uilt upon rtilly yservle wrkov heision roess nd uses the output of the mnoeuvre understndingF his proilisti frmework resons inluding pereption nd ehviour unertinty in the environment modelsF hese re used to predit the likely onsequenes of the utonomous vehile tions on its immedite environmentF roweverD Q the evlution of ll omintions of tions nd stte estimtions is omplexD thereforeD n online solver is used to otin n pproximtion of eh tion vlueF he vehile tions re evluted using set of rewrdsD nmelyX ollision riskD ehviourl riskD omfort nd tr0 rulesF e weighted sum of liner funtions is used to lne eh omponent of the rewrd funtion with respet to the vehile distne to the intersetionF st llows to dpt the ehviour of the utomted vehile to di'erent senriosF o vlidte the system performnes nd to determine uses of filure nd suessD uey erE formne snditors ssoited to the senrio re proposedF hese re prt of generi testing rhiteturesF he pproh is pplied to rossEutting senrios t rod intersetionsD onsidered very omplex nd hzrdousF imultion tehniques hve een used to evlute the proposed frmeworkD to exmine the lrgest numer of senrios nd to e tested in sfe onditionsF he result of this thesis shows tht it is possile to reson with other vehile ehviour in the deisionEmking proess while pprohing rod intersetion rossingF hile lssil methods fil to evlute the system ehviourD the proposed vlidtion method gives more insightsF st will llow to test the system on the rel rod s well s using more dvne dt driven methods in the deisionEmking modelsF R Résumé v nvigtion pour les véhiules utonomes implique que des déisions soient prises en permE neneD sur l se d9une onnissne prtielle et inertine de leur environnementF gei est limité pr les représenttions numériques de l9environnement et pr l9inertitude ssoiée u proessus de pereptionF he plusD il est di0ile de prédire le omportement des entités perçuesD prtiulièrement ux intersetionsF gette thèse propose un proessus de prise de déision prennt en ompte di'érents types d9inertitudesD notmment le omportement des onduteurs oservésD pour plni(er le mouvement du véhiuleF our omprendre le ontexte et les omportements des onduteurs oservésD une pprohe pr pprentissge utomtique est proposéeF ve résultt est utilisé pr le proessus de prise de déision pour onstruire une estimtion proiliste de l9environnementF ve mouvement du véhiule est plni(é en tennt ompte l9e'et que eluiEi pourrit voir lorsque le véhiule intergit ve d9utres entitésF xotre pprohe réompense les tions fvorisnt l9intertion et réduisnt les risquesF ve omportement du système est nlysé à l9ide d9un ensemle de métriques dérivées de l9nlyse du sénrioF our déduire le ontexte routierD des proessus gussiens pprennent des modèles de mouvement à prtir de trjetoires simuléesD qui inluent l9e'et des intertions entre les véhiulesF ves modèles sont segmentés en zones et utilisés pour omprendre le omportement d9un véhiule pE prohnt d9une intersetionF insuiteD des forêts d9rres déisionnels sont ppliqués pour estimer l mn÷uvre du onduteur dns hque zoneF v9ensemle de données utilisé pour l9entrinement est onstruit à l9ide de données enregistrées lors d9essis sur route et de simultionsF ges lssi(E teurs infèrent les mn÷uvres ltérles et longitudinles en extrynt des rtéristiques des trjetoires du véhiuleF gette pprohe montre que le ontexte routier méliore l lssi(tion des mn÷uvres et qu9en mélngent quelques trjetoires réelles et simuléesD il est possile de di'érenier les mn÷uvres de onduteurs rrivnt à une intersetionF ve proessus de prise de déision repose sur un proessus de déision mrkovien prtiellement oservle et les résultts de l ompréhension de l mn÷uvreF ge dre proiliste permet de prendre en ompte l9inertitude de l pereption et du omportementF geuxEi sont utilisés pour S prédire les onséquenes proles des tions des véhiules utonomes sur son environnement imméditF v9évlution de toutes les ominisons d9tions et d9estimtions d9étt est omplexeF r onséquentD un solveur online est utilisé pour otenir une pproximtion de l vleur de hque tionF illes sont évluées à l9ide d9un ensemle de réompensesD à svoir X risque de ollisionD risque omportementlD onfort et règles de irultionF ne somme pondérée de fontions linéires est utilisée pour équilirer hque omposnt de l fontion de réompense pr rpport à l distne du véhiule à l9intersetionF our vlider les performnes du système et déterminer les uses d9éhe et de suèsD des indiE teurs de performne ssoiés u sénrio sont proposésF geuxEi font prtie d9une rhiteture de tests génériques pour l vlidtion de système utonomeF hes tehniques de simultion ont été utilisées pour évluer le dre proposéD pour exminer le plus grnd nomre de sénrios et pour être testé sns dngerF ve résultt de ette thèse montre qu9il est possile de risonner ve le omportement d9utres véhiules dns le proessus de prise de déision à l9pprohe d9une intersetionF elors que les méthodes lssiques ne permettent ps d9évluer le omportement du systèmeD l méthode de vlidtion proposée donne dvntge d9informtionsF gel permettr de tester le système sur l route ouverte et d9nlyser plus en profondeur des méthodes sées sur l9intelligene rti(ielleF T Acknowledgement qetting to (nish this thesis ws long nd hrd work whihD without the support of lot peopleD would hve not een possileF pirst nd foremostD s would like to express my grtitude to my supervisors tvier snezEquzmnD ghristin vugier nd ylivier imonin for guiding me long this journeyD ut lso for hving shred with me their knowledge nd pssionF s would like to thnk enult for (nning this thesis nd for o'ering me position to ontinue my reserh longside the sme temF here is lso ll the people who work there E immnuelD elexndreD glementD ienteD pdyD vuilleD erunD hvidD ouF st tkes lot of people to mke vehile moveD thnk you ll for your helpF here re lso ll the people from sxse with whom s shred o'ee nd worked withX tenEelixD vusD teromeD emuryD hominiqueD wrioD itorD homsD elessndroF s ws not lone in experiening the highs nd lows of working on thesisD rvnnD idourdD wtthieu prnkD vudoF s m relly hppy tht we got through this togetherF wore spei(lly s would like to thnk vn nd hvid for ll the good times we hd hikingD trvellingD exploding kittens nd working outF o my fmilly nd friendD thnk you for lwys eing there nd supportiveF s would not e here if the womn who is now my wifeD hnxiD would not hve een so ptient nd helpful during these yersF hnk you so muhF

F F F F F F F F F F F F PP IFQ rolem pormultion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F PS IFQFI rolem ttement F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F PS IFQFP hesis epproh F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F PT IFQFQ gontriutions F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F PV IFR hesis gontent F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F QP PFPFIFI ehile gentri F F F F F F F F F F F F F F F F F F F F F F F F F F F F QQ PFPFIFP hriver gentri F F F F F F F F F F F F F F F F F F F F F F F F F F F F QR PFPFIFQ xetwork gentri F F F F F F F F F F F F F F F F F F F F F F F F F F F QS PFPFP hisussion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F QT PFPFQ heisionEmking for ehile gentri erhitetures F F F F F F F F F F F F F F QU PFPFR hisussion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F RQ PFQ fehviour nderstnding F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F RQ PFQFI he(nitions F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F RR
PFQFP wethods for fehviour nderstnding t od sntersetions F F F F F F F F RU PFQFPFI glssi(tion

F F F F F F F F F F F F F F F F F F F F F F F F F F F F F RW PFQFPFP roilisti qrphs F F F F F F F F F F F F F F F F F F F F F F F F F SH V Contents PFQFQ hisussion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F SP PFR epprohes to heisionEwking t od sntersetion F F F F F F F F F F F F F F F F SQ PFRFI uleEsed F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F SR
PFRFP roilisti epprohX rtilly yservle wrkov heision roess F F SU PFRFQ whine verning

F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F TH PFRFR hisussion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F TR PFS gonlusion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F TS
3 Manoeuvre Classication Applying Learned Functional Discretisation 67

QFI sntrodution F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F TU QFP puntionl hisretistion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F TV QFPFI wotion tterns ith qussin roesses F F F F F F F F F F F F F F F F F F UI QFPFP puntionl hisretiztion prmework F F F F F F F F F F F F F F F F F F F F F UR QFPFPFI htset equisition F F F F F F F F F F F F F F F F F F F F F F F F F UR QFPFPFP qussin roess rining F F F F F F F F F F F F F F F F F F F F F VH
QFPFPFQ hisretiztion of werging nd yverlpping rjetory F F F F F VP QFPFPFR hisretiztion of n epprohing rjetory

F F F F F F F F F F F VP QFPFQ hisretiztion esults F F F F F F F F F F F F F F F F F F F F F F F F F F F F F VQ QFPFR rhEwp torge F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F VS QFQ wnoeuvre glssi(tion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F VT QFQFI epproh F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F VU QFQFP ndom porest glssi(er F F F F F F F F F F F F F F F F F F F F F F F F F F F VW QFQFQ htset F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F WI QFQFQFI el invironments F F F F F F F F F F F F F F F F F F F F F F F F F F WI QFQFQFP imulted invironments F F F F F F F F F F F F F F F F F F F F F F F WP QFQFQFQ ryrid htset F F F F F F F F F F F F F F F F F F F F F F F F F F F F WP QFQFQFR petures F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F WQ QFR esults F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F WR QFRFI ndom porest opology F F F F F F F F F F F F F F F F F F F F F F F F F F F WT QFRFP puntionl hisretiztion gompred to etngulr hisretiztion F F F F WV QFRFQ gomprison ith fseline epproh F F F F F F F F F F F F F F F F F F F F F WW QFRFR esults ith the ryrid htset F F F F F F F F F F F F F F F F F F F F F F F IHH QFRFS hisussion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IHI QFS gonlusion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IIP RFQFI tteEpe F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IIR RFQFP etionEpe F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IIR RFQFQ rnsition wodel F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IIT RFQFR yservtionEpe F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IPI RFQFS yservtion wodel F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IPI RFQFT ewrd puntion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IPQ
RFR rtilly yservle wonteEgrlo lnningX en ynline ywh olver F F F F F IPU

RFRFI ywh olver F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IPU RFRFP ywg ilements F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IPW RFRFPFI wonteEgrlo erh ree F F F F F F F F F F F F F F F F F F F F F F IPW RFRFPFP pper gon(dene found F F F F F F F F F F F F F F F F F F F F F F IQH RFRFQ ywg elgorithm F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IQI RFS gonlusion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IQR
5 Implementation, Tests and Analysis 136 heisionEwking is one of the most hllenging tsks in utonomous drivingF ehiles need to dpt their ehviours in relEtime sed on their sitution understndingF he di0ulties in uilding nd understnding this digitl representtion of the immedite environment ome from the unertinties ssoited with the pereption nd the ehviour estimtion of the entities shring the sme rod networkF he fous of this thesis is deisionEmkingD s pplied to one of the most omplex rod network segmentsD rod intersetionsF he onvergene of multiple moile pltforms nd vulnerle entities onto single rod segment results in multiple ollisionsD due to the omplexities of the sitution understndingF sn this hpterD the rtionle for the deployment of utonomous vehile is formulted nd the deision for seleting the rossing of rod intersetions s the fous of this reserh is providedF he deisionEmking prolem is then formultedF st inludes the thesis sopeF pinllyD the ontriutions re listed s well s the orgniztion of the thesis outlinedF IP 1.1 Autonomous Vehicles: Social and Financial Rational he prdigms for humn moility hve hnged during the pst IH yersF hilst in postE industril ountriesD the need for personl vehiles is deresing due to the essiility to puli trnsport nd the inresing ost of owning personl vehileF sn other ountriesD like ghinD frzil or sndiD vehiles re still seen s sign of higher soil sttusD thus there is growing demnd for (rsthnd vehilesF iverywhereD rod networks re insu0ient to support the growing numer of vehiles nd ongestions osts nnully 6I trillion worldwide IF he impt of rod trnsporttion on people9s helth nd the environment is tking entrl stge on tody9s politil gend worldwideF o respond to these hllengesD (rst trnsformtion strted within the utomotive industry with the eletri(tion of pssenger vehilesF st ws supported y governmentsD giving inentive to help ustomers to hnge polluting vehiles for lener onesF st ws smll suess for ompnies like enultExissnD fw tht strted to propose eletri vehiles @eFgF lefD fw iQD oéFFFAF xew inititives from ompniesD suh s olvoD im to propose only eletriGhyrid vehiles from PHIW PF roweverD these re insu0ient to solve pollution prolemsF enother mjor hllenge is to redue the numer of identsF he trget (xed y the orld relth yrgniztion @ryAD nd followed y the iuropen unionD is to hlve the numer of idents y PHPH QF roweverD s illustrted in pigure IFID plteu ws rehed in PHIQF he predited impt of edvne hriving essistnt ystem @eheA nd utonomous vehile tehnologies shll redue the numer of identsF roweverD ehe systems re mostly ought y the middleEge popultion @euse of their ostA nd does not help to idents tht re minly used y young nd old people RF ttistis show tht most rod ftlities re due to humn errors QF hereforeD removing the humn from the vehile ontrol loop ould result in pigure IFP @A foss vehileD grnegie wellon niversity t hee rn hllenge xovemer PHHU UF @A teve whn the (rst leglly lind person to ride in qoogle9s utonomous rD wrh PHIP W here is pereptile elertion in this domin due to the strong interest y tehnologil ompnies like qoogle or moility providers like er nd vyftF hey re developing utonomous vehiles tehnologies with sustntil pitl investmentsF sn dditionD there is the emergene of new genertion of strtEups developing the relted tehnologiesF he utomotive industry seeks tody to eome moility providers rther thn vehile suppliers nd thus hnge their usiness model IHF e new industril segment is emerging round the domin of utonomous vehile tehnologies to provide softwreD omputing hrdwreD nd sensorsF he ene(t of utonomous driving for the puli mens tht more time will e ville for work or leisure inside vehileF hereforeD longer ommutes ould e more eptleF he provision of lnd trnsporttion servies for those whih nnot drive like the youngD elderly nd disled will inrese their life qulityF por professionlD delivery truk drivers ould e removedD reduing pries nd likely inresing goods trnsporttion e0ienyF o lssify utonomous vehile systems @eAD the oiety of eutomotive ingineers @eiA proposed S di'erent levels of utonomy IIF vevel P with ontrol of the longitudinl nd lterl motion ut with the situtionl wreness pilities monitored y humn driver is ville on tody9s highEend vehiles @eslD eudiAF he system omplexity emerges from the intertion etween the r nd driverD euse the system must ensure tht the driver n respond dequtely when requiredF e level Q prototype hs the sitution understnding nd r deisions hndled y vehile systemsF he driver is in hrge to monitor the system ndD in se pigure IFT erentge of ftlities y type of juntion in the iD fter IV here is geogrphi disprity with respet to these identsF por exmpleD in the uD juntions relted idents represented third of totl rod ftlitiesD wheres in prneD they ounted for IR7F purtherD IH7 of rodEftlitiesD were t juntions outside urn resD whilst in urn re they were twie s muhF he gei projet reords ll idents ross the iF st provides informtion nd sttistis tht led to etter understnding of idents t rod intersetions IWF st shows tht intertions etween vulnerle users nd vehiles re n ggrvting use of ftlities t intersetionsD tht is TH7 of ftl idents our with vulnerle rod users @pedestrinsD motoryleD iyleD etAF he wether nd night onditions re not n ggrvting ftor of ident t juntions ompred to other lotionsF yserving the ritil event tht led to ollisionD the most frequent prolem is timingX drivers did not perform ny tion @PR7AD hd premture tion @PI7A or ted too lte @IR7AD further detils re found in pigure IFUF he two min uses of this ritil event re fulty understnding of the sitution ndGor missed oservtionF st shows tht humns re likely shows its rws nd messges tht indite drivers to tke ontrol kF his system mintins the vehile in its lne nd mintins sfe distne with other tr0 entitiesF here is limited intertion etween driver nd vehile in the esl exmpleF e more intertive system tht inludes driver monitoring funtion hs een proposed y the rges tem t the ws QRF pigure PFQ shows the monitoring of the driver tivity nd risk ssoited with this tivity nd the driving situtionF his informtion llows to deide whether or not the driver pigure PFQ snterfes for driver entri intelligent vehiles with @A the rws for the estl euE topilot sking the driver to put his hnd on the steering wheelD@A driver monitoring displyD fter QR he prolem keeping humns within the nvigtion loop is tht drivers tsk shring is di0ultF yverEtrusting the pilities of vehiles is likely when drivers re exposed to few hllenging situtions QSF rung et lF proposed to show drivers hllenging sitution for the deisionE mking system to study how trust n e uilt QTF hen the system deides to pss the ontrol to the driverD the time tken to understnd the sitution nd to (nd the orret deision is di0ult to estimteF st depends on the driver ognitive lod nd its physil stteF sf the trnsition n e known in dvne @eFgF leving n e omplint reAD the trnsition n e prepredF roweverD during emergeny situtionsD up to IH seonds of response time n e expeted when pssenger is distrted nd might not e su0ient for sfety ritil situtionsF

SFI sntrodution F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IQT SFP esting prmework F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IQV SFPFI esting gontext F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IQV SFPFP esting erhiteture F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IRH SFQ uey erformne snditors to ivlute heisionEwking ystem F F F F F F F F F IRS SFQFI epproh F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IRT SFQFP fety us F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IRV SFQFQ rust us F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IRV SFQFR gomfort us F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ISH SFQFS xvigtion us F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ISH SFQFT erspetives on uey erformne snditors F F F F F F F F F F F F F F F F F ISI SFR ixperiments nd esults F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ISP SFRFI rmeters ixplortion F F F F F F F F F F F F F F F F F F F F F F F F F F F F ISP SFRFP fehviour ixmples F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ISR SFRFQ ivlution of the heisionEwking erformneF F F F F F F F F F F F F F F ISV IH Contents SFRFR ttistil wodel gheking F F F F F F F F F F F F F F F F F F F F F F F F F F ITH SFS gonlusion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ITR 6 Conclusion 165 TFI ummry of pindings F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ITS TFP gonlusions F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ITU TFQ erspetives F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F ITV TFQFI fehviour nderstnding F F F F F F F F F F F F F F F F F F F F F F F F F F F ITW TFQFP roilisti heisionEmking F F F F F F F F F F F F F F F F F F F F F F F F F ITW TFQFQ lidtion F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IUH

Cloud

Ps P P pigure PFR xetwork entri rhiteture fy introduing informtion from outside the ego vehileD the onstrution of the world model n e enhned nd thus sitution understnding improvedF P depends minly on knowing the stte of the emitting vehilesF woreoverD oopertive deisionEmking n e pplied to optimize the generl tr0 )ow in deentrlized mnner QUD QVF he vehiles under this type of opertion re known s network entriF he ultimte im eing to prtition the vehile intelligene etween the on ord omputer nd the loud s new ommunition links pper s Sq QWF his is expeted to hve high impt on the deployment of intelligent vehileF he impt of ommunitions to hieve sfer rod hs een explored in di'erent iuropen inititives RHD RIF e stteEofEtheErt on oopertive deisionEmking t intersetion hs een done y ghen et lFD it showed the potentil to improve sfety nd tr0 )ow RPF e new issue to e onsidered is the network seurity to void mliious ttksF purtherD shred informtion is sujet to other forms of unertinties ssoited to the ommunition hnnels nd shred informtionF ody mjor issue is the vilility of ommuniting vehiles nd equipped infrstrutureD they re rre nd ostlyF 2.2.2 Discussion he lssi(tion into di'erent rhitetures for intelligent vehilesD from nvigtion nd deisionEmking perspetivesD provides us with n insight into the level of sitution understndE QT ingD nd deisionEmking required if these is to e mde y omputerF he sope of this thesisD entres on vehile entri rhitetureF he rtionl is s followsX n utonomous vehile tht hs drivers s prt of the ontrol loop hs di'erent implitions more from n ergonomis perspetive due to the grey zone on the vehile ontrollilityF xetwork entri vehiles re fundmentlly vehile entriD with di'erent onstrints tht n e tken into ountF ht is ommunition issues @eFgF delysD lkoutD etAF hile ny of these two pprohes provides e0ient solution to the nvigtion nd deisionE mking prolemD works on vehileEentri system re more likely to inrese the utonomy of intelligent vehilesF he development of the three systems shll oexist in order to ddress ssoited hllengesF roweverD developments for vehile entri systems remins ore di0ulty for the deployment of utonomous vehiles for vevel Q nd RF uh vehiles drive longside humn drivers tht hve omplex ehviour nd need intert with itF he understnding of humn ehviour is disussed in etion PFQ nd ontriution on the detetion of driver9s ehviour is presented in ghpter QF e deisionEmking proess for vehile entri system needs to inlude intertion nd driver9s ehviour in order to drive in mixed nd unertin environmentsF his is presented in ghpter R with ywh to nvigte t rod intersetion rossingsF 2.2.3 Decision-making for Vehicle Centric Architectures sn vehile entri rhitetureD the nvigtion system mkes plns in order to reh destintion in sfe mnner RQF st tkes dvntge of digitl representtion of the environment uilt y the pereption systemF st lso needs to understnd its own stte nd urrent dynmis to pln the vehile motionF he stte of the nvigtion funtion system n e supervised y nother system to hnge its on(gurtionF et lstD trjetories generted y the nvigtion funtion re sent to the vehile ontrol system to rete the pproprite tutor ommndsF sntertions sn vehile entri rhiteturesD the nvigtion system is often divided into three suEsystems tht operte t di'erent rtes nd use di'erent sles of world representtion s in the RhGgX he route plnner (nds the pth from one lotion to nother using mpsF he ehviourl plnner determines the orret tions to e exeuted in spei( sitution @eFgF rossing n intersetionD pigure IFQAF sts environmentl model is mde using lneElevel mpsD pereption outputs nd ehviour models to understnd humn driver9s motionF he trjetory plnner genertes dynmilly fesile sfe trjetory with respet to ehviour plnner tions nd to immedite ollision dngersF heir outputs re used y the other suEsystem s gol to e hievedF heres the role of the route nd trjetory plnner re ommonly greed onD the ehviourl plnner is hrder to portryF gompred with the trjetory plnners tht ret to the immedite situtionD the ehviour plnner tkes deliertive pproh to pln with longEterm predition nd multiple hypothesisF husD its output n e of mny forms like wy pointsD motion reommendtionsD motion onstrintsD elertionsD etF pigure PFU shows the underlying rhiteture of typil nvigtion system for vehile entri solutionD inluding the informtion reeived from other systemF pigure PFU xvigtion funtion of vehile entri system with inputs nd perturtions RH 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing he pereption system genertes list of ostles representing the pereived entities nd ssoE ited ttriutesD s per the exmples in pigure PFVF ettriutes ssoited with these ojets inludeX type @eFgF vehileD pedestrinD etFAD pose @position nd orienttionAD veloityD etF his informtion is ssoited with knowledge dtEse to provide ontext @eFgF mpsA nd eE hviourl ttriutes @with respet to lerned modelsAF his type of representtion is used for the ehviourl plnner for long term preditionsF he trjetory plnner opertes t higher pigure PFIH puntions of vehile stte estimtion system with its typil inputs nd outputs RP his informtion is used to perform the di'erent nvigtion plnning tsksF por the rod interE setion rossing senrioD the route plnner level provides pth tht joins the urrent vehile position with the desired intersetion exitF he ehviourl plnner selets the longitudinl moE tion @eFgF veloity pro(lesD motion onstrintA to e performed given the priority rules nd other vehiles ehviourF he trjetory plnner exeutes tht plnD mintins the vehile in its lne nd modi(es the trjetory when unexpeted ostles or errnt ostle tions re detetedF

Discussion

hen few unertinties re onsideredD the sitution understnding for the ehviourl plnner n use simple predition models to onstrin the trjetory plnner in time nd speF roweverD t rod intersetion oluded vehile nd errnt driver ehviour re importnt soures of unertinties in mix tr0F hese re perturtions for plnners or fore the system to dopt onservtive ehviourF gonsidering proilisti inputs nd preditions into the evlution of the deision llow to serh for trdeEo' etween risk nd performnes RWF he ehviour plnner n lne low proility events with serious onsequene nd typil events with no thret to the vehileF es the vehile movesD multiple intertions will ourF st mkes the proilisti evlution of driver9s ehviour di0ult nd sujet to temporl hngeF he di'erent plnners tht onsist of the nvigtion funtion uses the mny ville inputs to generte nd evlute poliies to e exeutedF xone of these inputs n e preisely knownF husD plnners must e le to understnd nd reson with unertinties ssoited with mesurement nd driver ehviour to e le to lne etween risk nd performnesF yne of the di0ulties to pply proilisti resoning of the inputs is the lk of proilisti models for the other system onEordF hese re often pproximted with qussin distriutionsF yther types of unertinties re reted y mesurement proessing suh s flse positive nd flse negtiveD if not delt with the plnner might e errti nd dngerousF pigure PFPH imilrities etween ywh nd g node SV wethods to (nd n optiml poliy for ywh is in generl omplex nd intrtleF he omplexity of the prolem grows with the numer of sttesD oservtionsD tions nd the deision horizonF olver used to (nd this optiml poliy n e tegorized in twoX o1ine nd onlineF y1ine methods explore ll the model with ll the tions to (nd the optiml fter some time VRF heresD online methods serh the model for given mount of time nd return n pproximte optiml poliyF wethods to solve ywh more e0iently is n importnt reserh topiF hrun et lF used prtile (lter to represent the elief VSF henD the trnsition funtion is used only to model the trnsition of single prtileF his method is lso known s pointEsed lgorithmF gompting the elief to otin simpler prolem hs een used to improve the vlue estimtion VTF sn motion plnning prolemsD there re some sttes tht nnot e rehed from ertin positionD using this ide uurniwti et lF uild n e0ient solver for root motion tht mintins the elief only on rehle sttes VUF snsted of smpling from the eliefD the solver smples sttes tht re rehle y the rootF hese improvements on o1ine solvers were insu0ient to solve relElife prolemsD s the numer of itertions required to (nd the optiml poliy ws too igF ynline solver hs een found to dpt to mny types of prolemsF en exmple of online solver is the rtilly yservle wonteEgrlo lnning @ywgA VVF his method hs een omined with reinforement lerning to win ginst humns plying qo VWD WHF he vlue of eh tion is estimted y simulting the trnsition of prtile smpled from the elief for time horizonF he tion seletion is mde using n gfIF huring the tree explortionD the estimted vlue gets more urteF iven if eh node of the tree n e initilized without knowledgeD previously otined results n e used s prior estimtion of the vlueF roweverD the serh is only guided y the next tionD wheres some tions should e performed in ertin orderF hese models nd reent solvers enle the pplition of ywh in pplitions suh s rod intersetion rossingF he following presents the elements of ywh s pplied t rossrodF he desription of the stteEspe is often similr for vehile nvigtionF st inludes the physil stte of the vehile nd hidden vriles to represent their ehviourF por exmpleD drivers ehviour ws seprted etween stoppingD hesittingD norml nd ggressive in WIF hese were inferred from previously lerned ontextF por the physil stteD it is possile to work in ontinuous spe WPF roweverD their solution used lerned disretiztion to optimlly divide the spe into disrete vlues fterwrdF hrivers mnoeuvre tht ould e enfored y the regultion in ple is not onsidered in referred worksF ewrd funtions promote sttes nd tions tht mke the rossing mnoeuvre sfeF husD SW the rewrd funtion gives high vlue to sttes fter the intersetion nd negtive rewrd to ollision sttes WPD WQF eelertion hnges or devitions from referene speed my lso e penlized to otin etter poliies WQD WRF hese re not su0ient for the deisionEmking while interting with humnsF yservtion models re often similrD with qussin noises used to represent oservtion unerE tinties WQD WRF esoning with oluded ostles hs een hievedD ut ssumed (x numer of oluded ostles in the sene WPF ynline solver llowed to sle simple prolemsD with one or two vehiles WRD into models with multiple vehiles driving t multiElne intersetion WQF st lso llows the implementtion of ywh for relEtime pplition nd on test vehiles WRF ytherwiseD simulted environments re used to test these systemsF enother pprohD with o1ine solverD is to deompose the prolem into multiple simple ones WSF epplied to rod intersetionD single model is uilt eh entity nd solved o1ineF he strong dvntge of ywh is the possiility to reson with di'erent type of unertinties @ehviourD mesurementD olusionAF he model is lso expliit in term of intertion etween vriles nd judgment vluesF roweverD the rfting of these models requires mny prmeters to e tunedF gontrry to ruleEsed methodsD errors in the models does not neessry result in dngerous situtions ut rther mkes the evlution of poliies more omplexF pigure QFQ gomprison etween the pth of humn driver @purpleAD the pth stored in the mps @green nd redA t Eshpe intersetionF his pth is used to geometrilly determines the rossing zone @red reA nd the merging @ornge reAF he driver9s pth does not follow the lne enter nd void the rossing zoneF e rod rossing n intersetion n e prtitioned into three resX rrivl @or entrneAD rossing UH nd exitingF e very importnt re is s the vehile rrivesD this is where the deisionEmking proess engges the vehile mnoeuvreF hrivers need to e wre of their surroundingsD ny olusion use unertinty nd might fore them to slowdown or to enter hzrdous situtionF hriversD s they rrive to intersetionsD must redue their veloities s stted y the highwy ode even if they hve the priorityF he rtionl is to redue ollision risksF hrivers use the entrne re di'erentlyD some to slowdownD other to stopD or other to elerteF his leds to di'erent ouption rtes of the entrne zoneF hese n e identi(ed from driver trjetories nd then used to infer ehviourF he rossing re is where drivers re not supposed to sty s the ollision risk is highF hrivers deide to enter this re when they hve the priority over other vehiles pprohing nd estimte tht will hve to stop thereF peed is low for vehiles roessesD is used to infer the likely vehile poses t the rossrods for given durtionF e qussin roess @qA is olletion of rndom vrilesD where ny (nite numer of whih hve joint qussin distriution IIHF q is nonEprmetri modelD thus the predition step requires dt pointsF st ims to retrieve the funtionl dependeny f

Behaviour Understanding

V π (b 0 ) = ∞ t=0 γ t R(b t , a t ) = ∞ t=0 γ t E(R(x t , a t )|b 0 , π)F
(u t ) = v t + t from dtset D = {(u i , v i ) | i = 1, .
. . , n}F por simpli(tion purposesD the dtset is represented y two mtriesF is D × n mtrix tht ontins trining inputs {u i } n i=1 where D is the dimension of u i nd v vetor of size n ontining oserved vlues {v i } n i=1 F his dtset is used to represent the distriution of P (v * |u * , D) where u * n input nd v * orresponds to predition outputF he q is de(ned y its ovrine k(, ) @or kernelA nd its men funtion µ()F st n e written s GP (k(, ), µ()) withX

µ(u i ) = E[f (u i )] @QFIA k(u i , u i+t ) = E[(f (u i ) -µ(u i ))(f (u i+t ) -µ(u i+t ))] @QFPA
he men estimtion in eqution QFI is the oserved expettion of the proess for n inputF he ovrine in eqution QFP expresses how two inputs re reltedF wultiple kernel types n e usedF he squred exponentil ovrine funtion is hosen s it mkes loseEy smples highly orreltedD whih is expeted for motion ptterns SRD IHTF his is written sX

k(u i , u i+t ) = σ n ² exp(- 1 2l² (u i -u i+t )²) @QFQA
T i = t i L @QFRA with i ∈ N nd 0 < i < M D M
D = {(u i,n , v i,n ) | i = 1, . . . , M ; n = 1, ..., N } @QFUA W here u i,n = T i,n v n,i = (x i,n , y i,n , θ i,n )
o simplify the nottionD the vetor of trining input u n = {u n,i } M i=0 D nd the trining output re in vetor v n = {v n,i } M i=0

Gaussian Process Training

he lerning proess of q optimizes vlues for hyperEprmeters Θ = {σ, l}D tht re the noise level nd length sle to minimize the log mrginl likelihood @in eqution QFVA of the posteriori

distriution P (v n |u n , Θ)X VH log P (v n |u n , Θ) = - 1 2 u T n K -1 v v i - 1 2 log |K v | - M 2 log(2π) @QFVA
where

K v = k(u, u) + σ n I nd |.
| is the mtrix determinntF efter this optimiztion proessD the distriution of testing input N (µ * , Σ * ) n e found yX

µ * = k(u, u * ) k(u, u) + σ 2 n I -1 v n @QFWA Σ * = k(u * u * ) -k(u, u * )(k(u, u) + σ 2 I) -1 k(u, u * ) T @QFIHA
o simplify the trining proessD eh omponent of the feture vetor is ssumed to e indepenE dentF husD the motion pttern is the omintion of Q qs in iqution QFIID eh trined with single fetureF st holds for trjetories s the position longside the two xis is not dependentF he set of q is used to represent the motion pttern sX por the D its physil vriles re oservleF he oservtion of the distne to the interE setion dsv is found with mp mthing etween the urrent position of the nd lne level mpF he position is projeted onto the losest enter lne tht ends t n intersetion entrneF he other mesurement is the speed ŝsv F st is mesured either with spei( devies @swCqA or stndrd sensors emedded on ny vehiles nd shred over the gexEusF he vetor representing the oservtion is writtenX

f (T ) = [µ(T ), σ(T )] @QFIIA traj pred,n (T ) = {f x (T ), f y (T ), f yaw (T )} @QFIPA with T ∈ R D 0 < T < 1D n ∈ N D 0 < n < N nd f x ,
z sv = [ŝ sv , dsv ] @RFIUA
hese physil oservtions re lso otined for the yD however with more unertinty due to the pereption system limitsF he pereption system returns the position of the y tht is used in similr tehnique s the position of the to (nd the oserved distne to its intersetion entrne dov F sts speed ŝov n e mesured from rdr sensorD or y trking the vehile ross multiple onseutive imges or lidr frmesF ith the frmework of ghpter QD the y mnoeuvre is oservedF st gives n oservtion on the intended longitudinl mnoeuvre îov tht is used to estimte the elief on the true intentionF he oservtion vetor of the y tkes the formX The speed reward r s (s sv , d sv a) will rewrd the gent if its tions ring its speed loser to the referene speedD where the referene speeds S ref re otined y oserving the speed of single simulted gent pprohing the intersetionsF en exmple of pprohing yield sign is shown in pigure RFIIF st represents wht the gent should do if there is no other vehile @this rewrd does not use vriles from the yAF en gent whose speed is within ±2m/s is given the mximum rewrd due to its similrity with the referene speedsF sf the gent hooses tions tht redue the di'erene with the referene speedD it will get hlf the mximum rewrdF por other situtionsD there re no penlties s drivers n hoose their speeds t their onveniene within the highwy ode frmeworkF en exmple of this rewrd funtion is given in pigure RFIIF this is de(ned yX

z ov = [ŝ ov , dov , îov ] @RFIVA
r s (s sv , d sv , a)                R max |S ref (d sv ) -s sv | < 2 R max /2 S ref (d sv ) -s sv > 2 ∧ a > 0 R max /2 S ref (d sv ) -s sv < -2 ∧ a < 0 0 otherwise @RFPQA
The risk reward r r (s sv , d sv , s ov , d ov ) penlises situtions tht might hve two vehiles rossing the intersetion t the sme timeF he risk is mesured s the di'erene etween the time it tkes two vehiles to reh the intersetion entrne ssuming they do not deelerte or elerteF his sitution is onsidered s dngerous s ollisions our mostly when two vehiles enter the intersetion t the sme timeF his mesurement is similr to time to ollisionF o void risk nd mintin pssenger9s trustD gp of Rs is preferred IPW t stop intersetionD n extr I seond is dded for sfety onernsF his vlue is used s threshold over whih the gent gets the mximum rewrdD otherwiseD it gets the vlue of the time gpF he formultion of the risk rewrd is given in iqution @RFPRAF r r (s sv , d sv , s ov , d ov ) pigure RFII ixmple of rewrd return y the referene speed relted rewrd funtionF he shded re represents the rnge within whih the gent n get the mximum rewrdF en exmple is inluded of the vehile speed pro(le s it is rought into the mximum rewrd reF @A ewrds otined y the gent @in redA given the vehile tion @in lueA respeted s vehile elertionsF IPS le RFI ewrds relted to the ehviour of drivers with respet to tr0 lws

   R max | dsv ssv -dov sov -5s| > 0 | dsv ssv -dov sov | otherwise @RFPRA
e sv i ov top ield gross top H H R max GP ield R max GP H R max GP gross R max R max GP "R max
The expectation reward r e (e ov , i ov ) follows the work y vefevre et lFD this is sed on the notion of risk SQF hey estimte risk s the proility tht the intended nd the expeted mE noeuvre re di'erentF hen this proility is over HFQD the sitution is identi(ed s dngerousF sn our work this notion is emedded into the rewrd funtion @iqution @RFPSAA tht penlises sttes with di'erene etween e ov nd i ov F ytherwiseD the gent gets hlf of the mximum rewrdF r e (e ov , i ov )

   R max /2 e ov = i ov -R max otherwise @RFPSA
his rewrd inorportes the intertion etween the nd the yF he e ov depends on the physil stte of the nd the yF husD the n hoose tions tht hnge its physil stteD nd thus the expeted ehviourD to ring e ov to e the sme s i ov F his is one sidedE intertion with dngerous y ehviours tht is orreted y tions to redue this risk y mking the glol sitution less dngerousF

The intention reward r i (e sv , i ov ) reompenses sttes within the y nd the ehviour vriles tht omply with the highwy odeF he rewrd otined for eh vrile omintion is shown in le RFIF he intention of the other vehile is used euse it highly impts its motion @see iquE tion @RFIPAAF itutions tht re not dngerous with omined the y nd the ehviour @stopGstopD stopGyieldD yieldGyieldA ut not optiml in terms of intertion re neither penlised nor rewrdedF rlf of the mximum rewrd is otined for omintion tht re)ets orret interE tion thought it eomes unsfe if the motion is relessly exeuted @iFeF stopGyieldD rossGyieldAF wximum rewrd is ttriutedD if the y intends to stop while the is expeted to rossF st fores the gent to ross even if the risk is highF he sitution with oth vehiles trying to ross is highly penlised s there is no onsensusD thus ollisions might hppenF IPT ith this rewrdD the gent reeives inentives to tke tions so s to hnge its expeted ehviour to intert with the yF he gent needs to use every omponent of the rewrd funtion to refully pproh the interE setionD while hoosing etween performnes @r s Dr c AD risksD @r e , r r A nd intertions @r i AF his omined rewrd model is used y the solver to evlute poliies 4.4 Partially Observable Monte-Carlo Planning: An Online POMDP Solver he model in etion RFQ desries how the working environment funtionsF he sujet vehile shll (nd nd evlute poliies for the onfronted situtionF hese proesses re di0ult due to the lrge numer of sttesD unertinties ssoited with the oserved vriles nd the mnner the sitution might evolveF he ywh is solved using n online solver desried elowF 4.4.1 POMDP Solver e ywh solver evlutes poliies whih re sequenes of tions π = {a t , a t+1 , ...a t+n }F his evlution uses the model ywh modelD desried in setion RFQD to (nd the poliy vlue over time horizon onsidering its onsequenesF here is lwys n optiml poliy π * tht mximizes the vlue funtionF he solver (nds this optiml poliy or tries to pproximte itF he vlue of poliy V π is the sum of the disounted expeted rewrds following the poliy

desried in iqution @RFPTAF V π (b 0 ) = ∞ t=0 γ t R(b t , a t ) = ∞ t=0 γ t E(R(x t , a t )|b 0 , π) @RFPTA
where γ is the disount vlueD working s stop ondition for the evlutionF his vlue redues the in)uene of future rewrdD some exmples of its disounting e'et re shown in pigure RFIPF ith γ = 0D only the next rewrd is onsideredD whilst for nd with γ = 1 every rewrd hs the sme vlue nd the evlution stop when (nl stte is rehedF sullyD this vlue is set etween H nd ID to grdully derese the rewrd importneF e long serh horizon is more di0ult IPU pigure RFIP hisounting ftor for di'erent vlues of γ s it requires more itertionsF e prtil use of the disount ftor is to stop the evlutionD if disounting is too strong tht new rewrds re negligileF husD when the disounting ftor is elow @iqution @RFPUAAD the proess is stoppedF

γ t < @RFPUA
he time horizon H n e derived from iqutions @RFPVAF ln(γ H/∆t ) = ln( )

H ∆t ln(γ) = ln( ) H = ∆t ln( ) ln(γ) H = ∆t(ln( ) -ln(γ)) @RFPVA
with threshold ellow whih the serh is stoppedF IPV ith the rewrd funtion presented in setion RFQFTD there is no need for long serh in time s gent gets n immedite rewrd for its tionsF hi'erent vlues of this prmeter will e tested to optimize performnes in etion SFRFIF here exist two groups of solvers @see setion PFRFPAF ith the high numer of sttes in our model nd time onstrintsD y1ine solver ws not prtile solution VHF st requires to restrt the entire solving proess eh time prmeter hngesD furthermore this proess is intrtle in timeF roweverD it does not require omputtion when the deision hppenD nd optimlity n e gurnteedF en ynline solver is more prtil euseD it only tries to solve the prt of the model tht orresponds to the urrent situtionF his llows to solve lrge stte prolems nd is more relisti for the proposed vehile implementtionF roweverD there is no gurntee tht the poliy estimtion onverges for the omplete time horizon in the given time to hoose n tionD ut the estimtion on the short term horizon the evlution shll onvergeF his is eptle in our pplition s the evolution of the sitution is unertin for time horizon of SEIH seondsF gonsequentlyD the short term motion is well evluted while long term onsequenes re onsidered ut with lesser impt on the next tionF 4.4.2 POMCP Elements yut of the multiple lgorithms existing VSD IQHD IQID IQPD ywg developed y ilver et lF hs een hosen for its simpliity nd slility VVF he ore ide of their pproh is to use wonteEgrlo tree serh @wgA to trk the elief omined with n pper on(dene ound @gfEIA to diret the poliy serhF hese re explined elowF 4.4.2.1 Monte-Carlo Search Tree wonteEgrlo tree serh is heuristi serh lgorithm tht llows to evlute ywh poliiesF st uses genertive funtion to updte prtile stte to estimte the elief fter n tion hs tken pleF his is uilt from the trnsitionD rewrd nd oservtion funtion of the ywh model desried in etion RFQ withX

G(x, a) ∼ (x , z , r ) @RFPWA he tree is omposed of nodes T (h) = N (h), V (h), B(h) with h = {a 0 , Z o , a 1 , Z 1 , ..., a t , Z t }
the history of tions nd oservtions previously exploredF N (h) orresponds to the numer of IPW pigure RFIQ en exmple of wg treeD with two tionsD two oservtions nd SH sttesD fter VV times this history hs een exploredD V (h) is the vlue of h tht is the men return of umulted rewrd from simultions nd B(h) the elief represented y prtiles tht rehed this nodeF huring the serh timeD di'erent tions re pplied on stte smples from the urrent eliefF he genertive model is used to updte prtilesF ih time prtile rehes nodeD it inreses y one N (h)D updtes the vlue funtion with the umultive rewrd nd dds its stte the elief funtion of the node @more detils in RFRFQAF his proedure is repeted s mny times s possileF he struture of simpli(ed the tree is shown in pigure RFIQF

Upper Condence Bound

ndom tion smpling from the tionEspe during the serhD provides slow onvergeneF e guided serh to explore the poliy spe provides etter resultsF he pplition of the pper on(dent ound method fsten the onvergene y lning explortion nd exploittion IQQF snsted of hoosing rndom tion to updte prtilesD this method estimtes whih tion ould ring more informtion on the optiml poliyF por eh tion of the urrent node T (h)D n ugmented vlue riterion V @in iqution @RFQHAA is omputedF he tion tht mximizes this riterion is pplied on the prtileF tehniques is lso hllengeD uilding models is di0ult due to the system omplexityF woreoverD metris to ssess suh systems still need to e de(nedD there is no onsensusF hese prolems re urrently studied y demi nd industryD s it is neessry step for e deployment ITD IHQD IQUD IQVD IQWD IRHF st is onsidered strtegi issueD s whoever is ple to demonstrte the operility nd sfety of their utonomous vehilesD will hve mjor deployment dvntgeF he ojetive of this hpter is to provide n experimentl frmework to nlyse the performne of the proposed deisionEmking pplied to the rossing of rod intersetion s per its design ojetiveF his work hs ontriuted lso to the vlidtion proess developed within the ontext of the iuropen projet inleEQ 1 F his ddresses the vlidtion of eutomted gyer hysil ystems for utomotiveD helthD frmingD rilsD etF sn inleEQD ler seprtion etween the ystem nder est @A nd the esting ystem @A is mdeF wo issues were highlightedX he need of frmework tht exites the for multiple senrios @iFeF the testing speA nd the metris for the evlution of suh systemsF frinstorming in this projet highlighted the need for omined pprohD nmely physil trils nd simultionF sn the se of sfety ritil systemD the testing spe for physil simultions eing limitedF he hpter is divided into four prtsX IF he (rst rtionlises the testing frmework y explining the di0ulties fing the testing of the system proposedF he used frmework follows the rhiteture of ixefviEQF he testing frmework relies on simultions for the experiments to expnd the testing speD redue hzrdous situtionsD nd optimise ostsF PF he seond introdues uey erformne snditors @usAD these re metris sed on n nlysis of the senrio nd the expeted vehile ehvioursF por this purposedD four tegories re de(nedX sfetyD omfortD nvigtion nd trustF QF he third shows nd disusses results otined from the testing of the proposed deisionE mking proess using the uss de(ned in the seond prtF st shows results with di'erent 1 European Initiative to Enable Validation for Highly Automated Safe and Secure Systems IQU 5 Implementation, Tests and Analysis on(gurtions in three senriosF hese n e used to wht ould e done to improve the systemF en pplition of ttistil wodel gheking @wgA is inluded to extend the knowledge otined on the system performneF RF he ghpter onludes y providing perspetives with regrds to the evluted system nd the testing frmeworkF 5.2 Testing Framework

V (ha) = V (ha) + C log(N (h)) N (

Testing Context

es identi(ed y the xre IRID it is hllenge to inlude into n utonomous vehile proE ilisti nd nonEdeterministi lgorithms s they lk repetle system outputsF purtherD unE ertinties identi(ed in the rod intersetion rossing senrio @FfF etion IFPFQA led to hoose proilisti method for the deisionEproessF roweverD the omplexity of the models mde it di0ult to solveF st requires prtil explortion of the solution spe to hieve relEtime exeuE tionF gonsequentlyD the system is nonEdeterministi s the sme sequene of oservtions nnot e gurnteed to hve the sme ssoited poliyF his is mpli(ed y the intertion etween the nd the yF hen the tion of the hngesD the retion of the y doesD nd modi(es the sequenes of oservtionsF gonsequentlyD even with the sme initil onditionD the senrio n unroll di'erentlyF he min uses of rndomness re the elief smpling nd the genertive model @FfF etion RFRFQAF he time required to solve the prolem o1ine or the omputtionl power required to hieve etter onvergene my not e hievle inside test vehile with restrited omputtion powerF e more generl issue relted to nonEdeterminismD is tht the system is sujet to mny letory phenomenF husD in the sme environmentl onditionsD the sme stte mesured y the pereption my e slightly di'erent fter eing proessedF he letory ehviour of the system n e viewed s dngerous y instnes in hrge to grnt the right to test system on openF sn n industril nd iuropen minded ontextD the preution priniple previlsD nd ostly mesures must e tken for test to hppenF his is ompounded y the likely hzrdous onditions tht re to emerge s tests re performedF hese n result in the destrution of prototypes or injuriesF ithin this ontextD it is dministrtively very di0ult nd ostly to get uthoristions for testing in lose trk nd for operting in puli est trk experiments require trined drivers to operte vehiles nd to ret in se of dngerF ests re repetleD ut the ssoited logisti is umersomeF st hs the dvntge to ensure sensor responses (delity ut lk of ehviour (delity s humn gents follow previously greed plnF heployment in (eld opertionl testing is di0ult s the opertionl ost is high s well s the emergene of hzrdous onditionsF heir dvntge is to gin representtive kilometres of driving in rel environmentF st llows to gther informtion out system performne in edge ses not ounted during previous studyF roweverD it is impossile to reprodue senrioD s they hppen rndomlyF hese dvntges nd drw ks for eh method re ompiled in tle SFIF IQW ltform should llow to onsider the mesurement of s mny vriles s possileD espeilly those mesurement vriles tht represent the uey erformne snditors @ussA of the F he pproh is sed on the pility to over s mny di'erent senrios s possile y testing physil omponents to vlidte the models used for the simultion prtF his is sed on the premise tht testing for edgeEses nd hzrdous onditions is virtully impossile to do on puli rodsF imultion is onsidered e'etive though the modelling of sensors nd disturnes ould e omplexF 5.2.2 Testing Architecture he testing of the proposed pproh is mde using simultion tehniquesF his re sed on the vlidtion nd veri(tion rhiteture of the inleEsQ projet tht rings together the needs of di'erent domins @vitionD utomotiveD griulturlFFFAF sts ojetive is to redue ost ssoited with testing y using simultion nd y hoosing n dequte vlidtion methodologyF he uthor ontriuted to the formultion of this rhiteture nd ene(ted from the results from n industry wide frmework to ddress the vlidtion of egF his rhitetureD shown in pigure SFID omprises into three stgesX he test exeution pltform tht inludes the eg whih inludes of the @in our pplition the deisionEmking proessA nd the environment tht n e rel test trks or simultionsF he test mngement system uilds test ses to e performed y the exeution pltformF he test de(nition nd ontrol proesses provide the mens to hieve the tested senrioF hese n e rel mens or simulted models with di'erent on(gurtionsF his lyer lso inludes systems to reord mesurements of test runs nd to prepre this dt for PF hese vritions re mpli(ed y intertions etween the nd the yF he ywh tions hve n impt on the y tht might dpt its responseF st retes hin retion tht hnges the resolution of the senrioD even with sme initil onditionF purtherD the y seeks to void ollisionsF hese re rre eventsF gonsequentlyD some events might not pper in simultion nd like they re rre in rel life @eFgF ollisionsAF st will e di0ult to evlute metri sed on these eventsF QF iven if n optiml poliy existsD there re multiple lol suEoptiml poliies for the sme senrioF hese prevent the solver from (nding the optiml poliyD nevertheless these re eptle in driving termF hile drivingD there re often multiple solutions for the sme situtionF por exmpleD t yield intersetion oth stopping nd slowing down re eptle solutions s it optimises sfety for the former nd trvel time for the ltterF wetris sed on the intern performne of the system might not show how orret is the ehviour of the vehileD furthermore there might e high vrine due to these multiple solutionsF st lso mkes omprisons di0ultF

Approach

he metris proposed in this reserh seek to provide the mens to understnd nd vlidte deisionEmking proess pplied t rod intersetion rossingF heresD pprohes like the esponsiilityEensitive fety ims to gurntee the sfety of the vehile t ll osts IQUD the riteri proposed ims to evlute the system ehviourF uey erformne snditors @usA re de(ned s set of metris used to evlute the system in spei( senrioF ih us hs oundD used to identify when the system rehed orretD eptle or filed performne levelsF he eptle level disquli(es test if one us is elow the orret performne level ut remins elow thresholdF st llows to identifyD if the result is due to spei( sitution tht might require speil ehviourF por the rod intersetion rossing four us groups re identi(edX fetyD rustD gomfortD xvigtionF ih group hs t lest one usD ut the list is not exhustiveD nd more ould e dded when senrios eome more omplexF hree senrios re studiedD these re shown in pigure SFQF sn enrio eD the hs to yield to the y euse of the tr0 signF his senrio requires the to slow down to let the y rosses (rstF sn senrio f nd gD the hs the priority nd the y needs to yield @enrio IRT pigure SFQ tudied senriosD with the ujet ehile in lue nd the yther ehile in white fA or to stop @enrio gAF he di0ulty is to intert with the y tht might try ross while the is pprohingF his work ws deorrelted from the rewrd rfting done in etion RFQFTF st n e envisged to inlude uss into the rewrd funtionF his solution ws not retined for two resonsF pirstD there is n orgnistionl onstrintD in n industril ontextD the group hrged to develop the nvigtion system nd the group in hrge to vlidte re di'erentF his is to void developers to shpe their system to pss the vlidtion proedure insted of ful(lling the designted tskF he seond reson is tht similr prolems hppen when the gent tries to optimise for the testing riteri IHSF sn omplited environmentD the gent n (nd hk to optimise its rewrds gin y using unusul poliiesF sn our seD the system might deide tht stopping every time is the est strtegy nd would never ross the intersetionF ionomists fe the sme prolem nd formulted this ide s the qoodhrt9s lw stting tht when metri is used s trgetD it eses to e good metri9IHSF por nvigtion tskD n gent rewrded to redue the trvel time ould try to elerteD even if it is unsfeF he multiEojetive rewrd funtion used in redue its veloity to 0m/s when onstrined y the y mnoeuvreF st is importnt tht the does not stop for long durtion s it will lok the tr0F roweverD stopping might e required to llow the y to rossF his us is de(ned y the time the vehile me to stop efore rossing the intersetionF he durtion of the stop is mesured in the green re of pigure SFRF he us limits to determine the suessful opertion with this us re s followsF sn senrio eD when the hs the priorityD it should not stop for more thn Q seondsF sn senrio f nd gD the yield sign mkes the more likely to stop for longer timeD ut it ould lso slow downF hereforeD the limit is higher t S seondsF sn the highwy odeD there is no mention for mndtory time to remin stopped t n intersetionD even if there is stop sign 4 F husD these ounds re empirilly found from oservtion of the simultion nd oservtion on the test environmentsF ws pssed in WI7 of filed runs with the seline modelF his high frequeny shows tht this model is indequte for yield senrio @eAF he min reson for filure in senrio f nd g reltes to the qp usF husD the simulted model is unlikely to e trusted y pssenger or driverF his ehviour is unonventionl @mny stops nd smll gpAF his is likely used y n optimistion of the omfort nd of the trvel timeF he overll suess rte of the ywh is higher thn the simulted modelF sn the est seD the proposed system is etter y US7 for senrio eD y P7 for senrio f nd y RR7 for senrio gF xeverthelessD none of the on(gurtion mnge to reh suess rte higher thn VT7 @for senrio g with the on(gurtion IAF he two min resons of filure re the pssenger omfort nd the unsfe stop ussF he issue of omfort ours euse the ywh is not penlised nd the other driver mnoeuvre to pln the sujet vehile motionD s it rrives to rossrod intersetionF st shows how unertinties nd the ehviour n e used to hieve intertionF his frmework n dpt to di'erent senrio y hnging the prmeters of its rewrd modelF he performne is di0ult to interpret using lssil methods s the resulting system ehviour is nonEdeterministiF por this purposeD frmework for the testing nd vlidtion of the deisionE mking system ws developedF st is implemented using simultor tht models the other vehile ehviour nd environmentsD whilst the deisionEmking lgorithms @system under testA were

  oil nd pinnil tionl F F F F F F F F F F F F F F F F IQ IFP od sntersetions grossing F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IU IFPFI grossing t od sntersetion gompred to yther enrio F F F F F F F F IU IFPFP eident ttistis t od sntersetions F F F F F F F F F F F F F F F F F F F IWIFPFQ heisionEwking for grossing t od sntersetion F

  PW 2 Related Work on Behaviour Estimation and Decision-Making for Road IntersectionCrossing 31PFI sntrodution F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F QIPFP gontext pormultionX he xvigtion puntion sn sntelligent ehiles F F F F F F QP PFPFI glssi(tion of sntelligent ehile of erhitetures F

IHR W Contents 4

 4 Probabilistic Decision-Making, Applied to Crossroad Intersections 106RFI sntrodution F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F IHT RFP gontext nd sntegrtion into ehile gentri erhitetures F F F F F F F F F F F F F IHU RFPFI od sntersetion grossing enrio F F F F F F F F F F F F F F F F F F F F F IHURFPFP sntegrtion of the roilisti heisionEwking into the e erhiteture IIH RFQ heisionEwking prmework F

improvementsF IQ 1

 1 Introduction pigure IFI od ftlities within the i sine PHHID fter Q eserh on utonomous vehile me to wellEknown suess in the lte eightiesF iFhikmnsD s prt of the rometheus projetD hd pssenger vehile driving utonomously on motorwy ner ris in IWWR SF rogress in utonomous driving ould e mesured y the following milestonesX he hee qrnd ghllenge in PHHSD in whih for the (rstEtime utonomous pssenger unmnned vehiles trvelled more thn IHH km in the desert TF he hee rn qrnd ghllengeD where in PHHU severl unmnned vehiles operted in mok urn re UD in pigure IFPF pinllyD the otining of liense plte in gliforni @eA for the qoogle r in PHIP fter Q yers of development nd tests on open rod VD in pigure IFPF he whole sent ler messge to industry on the potentil of utonomous vehilesF ine tht dte glol interest on this domin hs explodedF IR

  of disenggement or dngerous situtionD orrets the vehile trjetoryF his type of prototype might never e distriuted for puli uses euse regulr driver might over trust the system IS or e distrtedF he reent ident in erizon etween er vehile nd pedestrinD hs shown the grvity of resulting ident IPF sn n erly stge of developmentD qoogleGymo hs identi(ed the prolem of humn drivers to e unsolvle nd imed for level R eF st implies tht under nominl ondition @good wetherD known reA the vehile is le to nvigte without humn interventionF ymo suE essfully demonstrted utonomous rideEshring system without sfety driver in eustin @eA in xovemer PHIU IQF hespite the U million of kilometres driven in puli rod nd illions in simulted environmentD they re still relutnt to ommerilize n unmnned vehile relted servie IRF everl yiws hve endorsed the sme pth nd re now fousing on this levelF he lrge ongoing e'ort without ommerilly ville servie demonstrtes tht remins severl issues to solve efore full deploymentF e level S vehile would e le to drive under dversril ondition suh s wether onditions nd would require new methods of sensingF feyond the oveEmentioned tehnil hllengesD dditionl issues lso hve to e ddressedX gost for hrdwre nd sensors needed to e instlled in vehile to eome utonomous re very highF his prie is ove the verge mount people re willing to pyD 6RWHHD for full utomtion in pssenger r ISF he industriliztion of ertin proesses might redue ostF roweverD there re still sensors to e designedD softwre to e developedD et to reh higher level of utonomyF lidtion nd veri(tion for the utomotive industry re sujet to strit normsF ih of utonomous systems dded to the vehile should to e tested nd vlidted under proE edures yet to de(ned ITF he domin of eutomted gyerEhysil system @egAD not only in the utomotive industryD lks vlidtion methods when it omes to mhine lerning lgorithms nd nonEdeterministi pprohesF oil eptilityD it hs een estimted tht suh tehnologies will diretly 'et the work of thousnds of people @txiD erD truk driversD etA s well s other domins like rel estteD energy supplyD etF ith no humns to supervise the deision of the vehileD the question of responsiility in se of ident is omplexF eidents might ontinue to hppenD therefore governmentsD insurers nd other stk holders shll disuss responsiility implition he onsequenes of e on urrent trnsporttion system hs een studied nd they re esE timted to hnge our wy of lifeF reliminry resultsD hve shown tht multiple tehnil hllenges reminD nd some sitution re more omplex thn othersF IT 1.2 Road Intersections Crossing od intersetions represent one of the most di0ult segments in rod networksF st is the rossing of di'erent tr0 )ows tht t ertin point ould onverge into ollision if pproprite deisions re not tkenF r0 refers not only to powered vehiles or twoEwheeler ut lso to the presene of pedestrinsF ih gent t n intersetion n e onsidered s hving di'erent ehviour in mny ses unmesurle whih mkes situtions very omplexF purtherD there re the issues of temporry olusionsD wether onditionsD poor lighting onditions nd the violtion of the tr0 rules for some prtiesF sn dditionD when rossing n intersetionD drivers negotite trough hidden gesture or ehvioursF por utonomous vehiles to e deployedD they need to ross rod intersetion sfely if they re to e epted into urrent tr0 onditionsF 1.2.1 Crossing at Road Intersection Compared to Other Scenario he di'erent rod situtions tht n utonomous vehile n enounter n e lssi(ed into (ve mjor groups s shown in pigure IFQF Traffic jam Lane following Pedestrians Lane merging Intersection pigure IFQ glsses of driving situtionD n utonomous vehile @in lkA n enounter @ssets from IUA e summry desription of this lssi(tion nd relted omplexity is given yX eA Trac jam, the veloity of the sujet vehile is smll s for other vehilesF gonsequentlyD their ehviour is to remin in their lne or to wit for the tr0 jm to endF ome drivers might try to hnge lneD ut ollisions re mitigted thnks to low speedF IU fA Lane following and highway driving re omplex euse of the reltively high speed of vehiles in the sene nd the potentil severity of resulting identsF he set of possile ehviours for eh vehile is reltively smllD the vehile hs to sty within their lne or to hnge lne if the tr0 hed is too slowF gA Driving in the presence of pedestriansD tht re pereived s the most vulnerle rod usersD in prtiulr hildren nd elderlyF edestrins n hve n errti ehviour thus driving next to them is di0ultF et intersetionsD pedestrins n e oludedD n ross t the lst minuteD etF por the pereption systemsD this is hllengeF eutonomous vehiles will slow down s they re next to pedestrins hA To merge with trac, the sujet vehile must estimte the gp etween two vehilesF st is ommon for humn driver to engge merging with smll gp nd to expet the other vehile to llow the mergingF his ehviour estimtion is omplex s drivers might not e willing to ooperteD resulting in di'erent mnoeuvresF iA Road intersectionsD the sujet vehile is left with di'erent mnoeuvres @stopD yieldD rossD turn leftGrightAD tht will 'et the ehviour of surrounding vehileF purtherD some vehiles might e oluded or their ehviour vrileD leving the utonomous vehile to deide nd dpt onstntlyF he most di0ult sitution rises when the sitution omplexity nd unertinty re highF his is the rossing of rod intersetion s shown in pigure IFRF pigure IFR od situtions omplexity nd unertinty he (gure IFS shows the di'erent vriles tht rete di0ulty t n intersetionF he most IV often studied hnge inludes vrition of tr0 sign nd the presene of other vehilesF he presene of pedestrin or two wheeled vehiles n mke senrios very omplexF st is di0ult to design sitution understnding nd deisionEmking system ple to ddress suh omplex situtionsF pigure IFS ilement nd entities providing vriety to the intersetion use se 1.2.2 Accident Statistics at Road Intersections od injury is the Vth use of deth ross the world popultionD thus governmentl nd nonE governmentl orgniztions keep trk of ident reports to rise wreness nd guide projetsF sn the i lmost PH7 of ftl idents our t rod juntions IVD s shown in pigure IFTF por the purpose of the thesisD three types of intersetion hve een identi(edX EjuntionsD rossrods nd roundoutsF ith new legisltion nd new vehile sfety devieD the numer of ftl idents t juntions hs een redued y RH7 sine PHHT to reh SHHH in PHISF

  to ehve di'erently to wht n e expeted regrding the ontextD resulting in dngerous situtionsF husD humn errors re likely t the origin of most ollisions t rod intersetionsF PH histriution of ritil events t rod juntionsD fter IV he lyout of typil rossrod intersetion results in PR likely ollision pointsF od designE ers introdued roundout whih redue them to RD s shown in pigure IFVF prom sfety perspetiveD roundouts redue side ollision tht represent the most mortl sitution when roundouts did not exist PHF roweverD drivers hve muh di0ulties on understnding situE tion when entering roundoutsF pigure IFV ehile likely ollision points omprison t rod intersetion nd roundoutD fter PI iven if no sttistis exist for utonomous vehileD they hve een often involved in ident PI t rossrodsF he (rst ollision etween two utonomous vehiles hppened t the rn hee hllenge of PHHU PPF he identD illustrted in pigure IFWD ws due to the vehile kynet stopping used y inoherent dt ssoition etween the mp nd pereptionF et the sme timeD the los vehile proeeded to overtke the immoilized kynetD entering its lind spotsF efter kynet (nlly found pth to quit the intersetionD it ollided with los tht expeted kynet to remin sttiF sn this exmpleD sensing unertinties plyed key role in the ollisionF huring the eventD there were lso S reported lose lls or ollisions tht ourred t rod intersetionsF pigure IFW gollision senrio etween los nd kynet during the urn hee hllenge PHHUD fter PP eutonomous vehiles funtionlities hve een tested on puli rods sine PHIHF he esl ident @wy PHITA ws due to driver inttention whilst the utopilot did not detet the truk hed rossing lnes PQF et other instnesD ollisions hppened ut the utonomous vehile ws not t fult PRF por exmpleD in eptemer PHIT vexus from ymo ws involved in rodside ollisionD used y nother driver rossing red lightF sn wrh PHIUD driver filed to yield to the er vehile t n intersetionF he exmple shows thtD even if the ehviour of the utonomous vehile is orretD the understnding other vehile intentions is key to sfe rod intersetion rossingF 1.2.3 Decision-Making for Crossing at Road Intersection sntertions etween n intelligent gent nd the environment re mde of three funtionsX pereptionD deision nd ontrolD s shown in pigure IFIHF heisionEmking is the most omplex PP s it depends of the performne nd error of the other systemF por oth humns nd utonomous systemsD wrong deisions ould led to identsF pigure IFIH sntertions etween n intelligent gent nd the environment et rod intersetionD the (rst deision tken is the diretion tht will hieve the driver9s desE tintionF his deision tkes the form of routeD sequene of rodD tht is hosen from mp informtion nd tr0 monitoring to reh n ojetive onsidering trvel timeD prieD pollution expositionD FFF prom this route the position of intersetions n e foundF et the lol level of the intersetionD the vehile must deide on its trjetory to ross the intersetionF his trjetory is omposed of the pthD olletion of positionD nd ssoited temporl informtion @speedD or timeAF he pth is found from the lterl mnoeuvre tht deide if it is neessry to move wy from the urrent lneF he temporl omponent of trjetory is omputed fter deiding on the longitudinl mnoeuvreD tht is t rod intersetionX to stopD to ross or to yieldF purthermoreD oth mnoeuvres re hosen sed on the understnding of the environment surE rounding the vehileF everl entities intert t n intersetionX vehilesD pedestrinsD tr0 infrstrutureD etF hese entities 'et the utonomous vehile mnoeuvreD whilst its mnoeuvre lso 'ets the environmentF husD deisions must e onstntly updted to ret ppropritely to these intertionsF ome mnoeuvre nd intertion re undesirleD for exmple two vehiE les interting together nd ttempting to enter the intersetion is undesirleF hereforeD the utonomous vehile shll itertively dpt its trjetory or hnge mnoeuvreF ereption systems re imperfetD s result degree of unertinty risesF his 'ets the understnding of the situtionD thus the deisionEmkingF he e'et of deisions of the sujet vehile on itself is di0ult to predit euse of the vehile dynmi responseF purtherD the PQ predited e'et of n tion on other environment elements is lso di0ult to predit euse of intertionsF oures of unertinties tht hllenge deisionEmking re shown in pigure IFIIF hese soures n e divided in four tegories PSX IF gon(gurtion sensing 'ets the estimtion the system hs on its own stteF st is used y proprioeptive sensor noiseF PF gon(gurtion preditility omes from the di0ulty to estimte tutors responses nd 'et the estimtion of future vehile sttesF QF invironment sensing is sujet to sensor noise nd environmentl onditionF st 'ets how well the rel sene is estimtedF RF invironment preditility ounts for intertion nd other vehile ehviour tht mkes future sitution di0ult to preditF pigure IFII oures of unertinties nd informtion tht hllenge the deisionEmking system PR he deisionEmking system relies on priori informtion to filitte the seletion proessF prom mps the priority regultion or the intersetion lyout n e known in dvneF verned models re used to enhne the understnding ville on the sitution @eFgF to infer other drivers ehviourAF 1.3 Problem Formulation 1.3.1 Problem Statement he mjor tsk for n utonomous vehile is to intert with its environmentF he resulting ehviour leds to whether suh vehiles n e soilly eptle in terms of performneD sfetyD understndle for other rod userD etF his is the produt of the deision tken y the vehile s it nvigtes to its destintionF he deisionEmking proess tkes ple in relEtime through the oservtion nd understndE ing of n inomplete nd unertin digitl representtion of the environment @world modellingAF here is unertinty ssoited with the environment tht results from sensor physis nd lE gorithms onstrintsF sn dditionD there is unertinty with respet to the environment preE ditility used y the e'et of vehile tions nd the ehviour of other intertive gentsF gomined with the unertinty ssoited with the sujet vehile on(gurtionD the ensemle of possile futures is omplex to exploreF gonsequentlyD only n estimted vlue of deision is ville for the seletionF he prolem resides on how the expeted vlue of deision n e estimted so the orresponding vehile tions n e hosen sfelyF hetermining whether solution to the deisionEmking prolem is lso omplexF sf one limits to rossing only rod intersetionsD the senrio spe rpidly eomes very lrgeF esting with physil vehiles eomes impossileD there is no repetly nd even setting simple intertion requires multiple resouresF hereforeD mens to ssess suh deisionEmking system re neededF he prolem rises on the mnner tht deisionEmking system n e evluted to provide trust on the system under testF he prolem ddressed in this thesis entres on the mnner n utonomous vehile deides tken into ount ontextul informtion nd unertinties ssoited with the vehile ehviour nd its opertionl environmentF e onsequene of the stohsti nture ssoited with the unertinty is tht the performne nlysis of the resulting deisionEmking system must inlude the nonEdeterministi result of the pprohF PS he purpose is tht urrent deisionEmking pprohes fil to reson with unertin informtionD thus their ehviour might not e soilly eptle or understndle to pedestrinsD drivers nd pssengersF purthermore in suh onditionD it is indeterminte if there exists single optiml poliy tht stis(es ll the requirement for the driving tskF husD the system hs to selet solution mongst set of vlid poliiesF he rod intersetions rossing useEse o'ers omplex nd hllenging senrios for utonomous vehiles tht requires the interprettion of driver9s intentions nd to del with omplex intertionsF he ojetives were (rst to investigte potentil mthemtil formultion of deisionEmking tht n e pplied to utonomous vehile nvigtion system nd more spei(lly how they were used t rod intersetion rossingF eondD to propose novel method for driver9s ehviour understnding dependent of the environment ontextF hirdlyD to design deisionEmking system tht n use the driver9s ehviour informtion nd dels with the mny unertintiesF imerging from the di0ulty to evlute the performnes of the systemD the lst ojetive ws to (nd systemti wy to evlute the performnes of the proposed solutionsF 1.3.2 Thesis Approach he pproh undertken follows the industril ojetive to otin prtil implementtion of the developed solution nd the sienti( ojetive to (nd solution for deisionEmking under unertintiesF he (rst step ws to determine nd formulte the rod intersetion use se to e used s the fous of the development in terms of the deisionEmkingD solutionD experimentl sis nd evlutionF st inludes n nlysis from the literture with regrds to identology dt nd its omplexityF his is entred on the rossing of the intersetion y the sujet vehile @A whilst onsidering the likely presene of yther ehile @yA rossing or stopping perpendiulr to F he seond step onsisted in reolletion of (eld experiene dt from rossing rod intersetions pplying driving ssistne funtions nd utonomous ehviour mongst enult engineers s well s preliminry experienesF he purpose ws to understnd the physil onstrints relted to vehile systemsF hey lso provided some insight on pereptionD lolistion nd mp systemsF prom there onurrent literture review ws done on the pprohes used for deisionsEmking pplied to intelligent vehiles nd ehviours estimtionF yut this study fous ws mde on the theory ssoited with proilisti pproh in order to determine the tehniques tht inludes unertinty nd inorporte it to the deisionEmking proessF he results led to model PT the system under study sed on rtilly yservle wrkov heision roess @ywhAF o represent the unertinty ssoited with the yD mhine lerning lgorithms re used to estimte its ehvioursF he proposed solution is then formulted s deisionEmking frmework together with its underlying theoryF pigure IFIP shows n overview of the frmework s prt of n utonomous system vehile rhiE teture to provide ontextF pigure IFIP he sitution understnding nd deisionEmking frmework within the ontext of the utonomous vehileD in green elements with the thesis ontriution he solution pertinene needs to e vlidtedF es utonomous vehile re omplex systems their vlidtion is di0ult nd remins n open questionF sn n e'ort to ddress this issueD systemti pproh for testing ws developedF st resulted in the de(nition nd seletion of uey erformne snditors @usA nd the usge of sttistil model heking to get more insight on the performnes of the system in the rod intersetion use seF he (gure IFIQ shows elements of the evlution frmework nd their intertion with the system under testF PU pigure IFIQ ivlution pipeline for the deisionEmking frmework tht is the system under testF he test genertion prt llows n explortion of the senrio spe nd reon(guE rtion of the systemF efter multiple experiments tres re nlysedF 1.3.3 Contributions he sis of the proposed solution resides on proilisti deisionEmking frmework ppliE le to rod intersetion rossingF st provides new pproh for ehviour understndingF wethods from other domins hs een used to understnd the performnes of the deisionE mking frmeworkF gontriutions re s followsX e frmework to uild funtionl disretiztion of the driving spe is presented in hpter QFP PTF st is sed on the use of qussin proesses to lern the motion ptternsF st n e pplile to ny type of rod intersetionF he omptiility of this representtion with respet to lne level mps used in enult9s vehiles hs een shownF he inferene of driver9s intention pprohing n intersetion is presented in hpter Q PUF st lssi(es the lterl nd longitudinl ehviour from fetures of the driver9s trjetoryF e hyrid dtset inluding simulted nd rel trjetories ws used for trining purposeF e deisionEmking frmework for rod intersetion rossing tht inludes resoning with on(gurtion nd preditility unertinties is presented in hpter R F st uses highElevel ehviour oservtions from the lterl nd longitudinl lssi(erF st dpts the vehile veloity with respet to vrious driving riteri @eFgF omfortD riskFFFAF e frmework to test deisionEmking systemsF st inludes the formultion of uey erforE mne snditors to interpret the system performneF PV hese works resulted in R pulitions t interntionl onferenes nd I presenttion in workshopX wF frierD gF vugierD yF imoninD nd tFsñezEquzmánD puntionl disretiztion of spe using qussin proesses for rod intersetion rossingD in PHIT siii IWth snternE tionl gonferene on sntelligent rnsporttion ystems @sgAD PHITD ppF IST!ITPF wF frierD gF vugierD yF imoninD nd tF sñezEquzmánD glssi(tion of drivers mE noeuvre for rod intersetion rossing with syntheti nd rel dtD in PHIU siii sntelliE gent ehiles ymposium @sAD PHIUD ppF PPR!PQHF wF frierD gF vugierD yF imoninD nd tF sñezEquzmánD roilisti heisionEwking t od sntersetionsX pormultion nd untittive ivlutionD in PHIV snterntionl gonferene on gontrolD eutomtionD ootis nd isionF wF frierD tF uileufD vF ummelhrdD eF enzgliD eF igwrD gF vugierD yF imoninD eF vegyD nd tF sñezEquzmánD lidtion of ereption nd heisionEwking ystems for eutonomous hriving vi ttistil wodel ghekingD in PHIW siii sntelligent ehiles ymposium @sAD PHIWF tF uileufD wF frierD vF ummelhrdD gF vugierD eF vegyD fF fudouinD F qenevoisD yF imonin nd tF sãnezEquz ¡ mnD lidtion of ereption nd heisionEwking wethE ods for eutonomous hriving using ttistil wodel ghekingD in IHth orkshop on lnE ningD ereption nd xvigtion for sntelligent ehiles t siiiGt sntF gonfF on sntelE ligent oots nd ystemsD ytoer PHIVD wdridD pin 1.4 Thesis Content he remining of the thesis omprises in six hptersX ghpter P presents relted work on deisionEmking pplied to the rossing of rod intersetionD the (rst setion reviews pprohes for ehviour estimtion nd the importne of the rod ontextF he seond setion presents lssil pprohes nd key dvnements on deisionE mking t rod intersetionsF imphsis is mde on proilisti pprohes euse of their prtil representtion of unertintyF he lst setion disusses the hosen pproh with respet of the stte of the rts nd provides the rtionleF PW ghpter Q presents mnoeuvre lssi(tion frmework uilt upon funtionl disretiztionF he (rst setion presents priniple for lerning motion pttern with qussin proessF heir pplition to disretise the spe of rod intersetions is then presentedF he seond setion presents the use nd dvntge of rndom forest lssi(er for mnoeuvre lssi(tionF o overome the need for lrge dtD we show the dvntge of using hyrid dtset ontining simulted nd rel dt to trin the lssi(erF o onlude this hpter quntittive nlysis of the overll frmework is mde ginst seline lssi(tion methods nd other types of spe disretiztionF ghpter R presents the deisionEmking proess onsidering mnoeuvre unertintyF sn the (rst setionD the integrtion of rtilly yservle wrkov heision wking proess in vehile entri rhiteture is disussedF he seond setion desries eh element of the rtilly oservle deision proess nd their model hosen is disussedF o onludeD the online solver used for the pplition is presentedF ghpter S presents unique evlution pproh pplied to vlidte the proposed deisionE mking frmeworkF he detil of the testing frmework is detiledF uey erformne snditors developed to quntify evluted the proposed pproh re detiledF esults otined from lrge numer of simultions re disussedF e lso pplied ttistil wodel gheking on our result nd we show how it helps to understnd the result of our pprohF ghpter T onludes the thesis y summrizing the ontriutionD inludes ritique on the (ndings s well s desription of future workF QH

  on Behaviour Estimation and Decision-Making for Road Intersection Crossing (a) (b)

  etween eh system re shown in pigure PFS F QU 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing pigure PFS ehile entri rhitetureD fter QQ he nvigtion module mintins plns on di'erent time nd sptil slesF he rhiteture proposed y weystell et lF distriutes nd groups these plnning funtions given the time nd spe sle RRD s shown in pigure PFTF puntions re implemented s omputtionl nodes using the relEtime ontrol system @gA rhitetureF ih node proesses its sensory input di'erently s they operte on di'erent time sleF hort term @IEQ seondsA plnning requires simple ut urte environment representtionsD wheres deisionEmking nodes reson with long term preditions nd unertin informtionD n exmple is shown in pigure PFTF he suE proesses of g nodeD shown in pigure PFTD re required for ny deisionEmking systemF here exist interonnetions etween lyers to mintin oherent pln if one lyer is unle to exeute the request tskF st shows tht the deisionEmking is not n isolted tskF snternllyD it onsists of di'erent funtions tht mintin n pproprite representtion of the environmentD the neessry knowledge to evlute plns nd proess to generte themF ixternllyD it works with other deisionEmking systems tht work on di'erent slesF QV 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing (a) RCS node internal structure, after [44] (b) Layered architecture for situation understanding, after [33] pigure PFT RhGg eferene wodel erhiteture for unmnned vehile QW 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing

  rte nd n ene(t of simpler representtions suh s dynmi oupny grid or shortEterm preditionD s shown in pigure PFVF (a) Object list with classied object type, after [45] (b) Probabilistic occupancy grid with dynamic cells (red), free space (blue) and unknown (green), after [46] pigure PFV hi'erent types of output from pereption systems e nvigtion system uses Epriori knowledge of its immedite surrounding in the form of righE RI 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing he(nition mps @rhEmpsAF hese re preise lneElevel representtions of the rod geometry together with ttriutes tht indite the expeted driver ehviour t given positionF en exmple of suh representtion is shown in pigure PFWF pigure PFW righEde(nition mp from enult of rossrod tht inludes diretion llowed nd lnes mrking @in lueA snformtion on the vehile stte is lso updted s this determines the vehile pose nd veloityF ehile lotion nd heding n e estimted using qx reeiverD vehile odometryD struture from motion mers nd point louds RUD RVF pigure PFIH shows typil struture of vehile stte estimtion system for utonomous vehilesF

  sn vehile entri rhitetureD the retion nd evlution of poliy requires estimtions nd preditions of other entity sttesF imple dynmi models n explin their shortEterm motionD RQ 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing however to predit on longer horizonD ehviour understnding is neessry SHF his setion presents de(nitions of terms ssoited with driver9s ehviour in rod intersetion rossing senrio nd the min pprohes for its understndingF 2.3.1 Denitions o study the ehviourD it is neessry to understnd the ognitive proess from whih humn genertes tionsF st n e divided into three stgesX killD ule nd ixplortion SIF pigure PFII shows the resoning whih results in n tion used y new sensory inputF he immedite response from the skill level is importnt for ollision voidne nd shortEterm motion plnning @eFgF lne followingAF roweverD these tions re not the result of longEterm plnF ithin the other two levelsD humns need to understnd the sitution nd tions of other drivers to generte the desired motionF pigure PFII vevels of ognitive ontrolD on how humns proess informtion from the environment nd generte deisions nd tionsD fter SPF

M

  lon = {Stop, Y ield, Cross} @PFPA et rod intersetionD the lterl mnoeuvre hnges the priority tht pplyF st is neessry to understnd it prior to the longitudinl mnoeuvreF pigure PFIP ehile veloity pro(les when driving towrd n unsignlized intersetionF hese re extrted from the dtset presented in ghpter QF he driver Attitude indites how the driver is responding to the situtionF hen exeuting mnoeuvreD the ttitude of the drivers give informtion out the ggressivityD the mentl hrge of the driver or the oherenes of his tions with the situtionF por most of the senriosD mnoeuvre n e found to void ollisions nd to mximize the trvel timeD howeverD humns re often unle to dopt the dequte ttitudeD willingly or unwillinglyF gonsequentlyD TH7 of idents re due to driver9s tionsD s doumented in etion IFPFPF fehviour ttitude is mjor soure of preditility unertintyF et rod intersetionD the desription of driver ttitude n e of severl typesD suh s ggressiveD hesittingD sportiveD inttentiveD et SQD SRD SSF RT he ttitudeD s desried y vefevre et lFD di'erentite the driver intention nd expectation to infer level of risk SQF he intended mnoeuvre represents wht is the driver9s urrent mnoeuvreF he expeted mnoeuvre is the prole mnoeuvre required y the situtionF he proility of the two vriles eing di'erent is used s n inditor of risk ssoited ttitude of the other driverF enother spet of driver ttitude is the driving styleF st helps to predit sudden hnges of tion or tions outside their typil rngesF here re vrious wys to desrie it suh sX eggressiveGxormlGresitting SS or evergeGportyGelxed SRF eltion etween eh element of the ehviour is synthesized in pigure PFIQF st is neessry to evlute these spets of the ehviour in order to inrese the predition horizon ut lso to hve etter sitution wrenessF to desrie driver9s ehviour 2.3.2 Methods for Behaviour Understanding at Road Intersections rior to the ehviour understnding funtionD two fundmentl proesses re neededX the vehile detetion nd vehile trking steps STF he former identi(es ostles out of the sensors mesurementsF gurrent stteEofEtheErt methods uses lerning lgorithms to trin models to detet vehile slient fetures in mesurements RSF he ltter trks vehiles ross multiple frmesF st llows to reEidentify nd to otin the pst trjetory of the vehileF st uses estimtion methods @ulmn (lterA to fuse nd ompenste mesurement errors from multiple sensors SUF ehiles detetion nd trking funtions re onsidered s prt of the vehile pereption systemD RU wheres the vehile ehviour reognition prt of the world modelling funtionF hese steps nd proessed informtion re shown in pigure PFIRF pigure PFIR ehile understnding nlysisD from ST fehviour identi(tion from pst situtions is minly done pplying lssi(tion pprohes nd proilisti grphsF he di'erenes etween the two pprohes re illustrted in pigure PFISF foth pprohes re desried in detil in the following setionsF pigure PFIS op lyer represents proilisti grph pprohD the dsh irles desrie the hidden vrilesF he ottom lyer shows the lssi(tion stepsF petures re omputed from pst oservtionsD these re used y the lssi(er from whih the ehviour identi(tion is done RV 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing 2.3.2.1 Classication e lssi(tion pproh uses fetures tht n e omputed out of oservtions mde on the vehile trjetory to di'erentite etween di'erent lsses of longitudinl nd lterl mnoeuvresF sn generlD most estimtes re sed on proprioeptive vehile sensors tht llow to mesure veE loitiesD elertions nd pose SVD SWF reding nd position in the lne hve more importne for lterl mnoeuvres lssi(tion THF yservtions on the environment nd rhEmp inforE mtion provide ontextD prtiulrly when rriving to omplex res s intersetionsD improving the mnoeuvre lssi(tion proess SWF gex us informtionD pereption system on the ror instlltion on the infrstruture n e used to uild trining dtsetF he driver stte lso ontriutes to the lssi(tionD tht is its gzeD tution on the vehile ontrols @eFgF elerE tor or rke pedlA TIF roweverD its uses privy issues nd suh lssi(ers re reltively dependent of spei( driverF vterl mnoeuvre t rod intersetion rossing n e lssi(ed with di'erent lgorithmsX upE port etor whine @wA TPD ndom porest glssi(ers @pgA SVD neurl network THD TQF gurrent stte of the rt for lssi(tion re neurl net used y hilips et lF to lssify the lterl mnoeuvre t multiElne intersetions THF hey extrt fetures of the vehile stte for the lst Q seonds nd the tr0 stte reorded from video mer surveillneF hey trined vong hortEerm wemory networks @vwsAD otining n ury of VS7 t up to ISHm from the intersetionF huring the trining the position of the vehile weights more thn ny other feture to understnd the lterl mnoeuvreD furthermore t multiElne intersetionF st questions the neessity to use IHR fetures to trin their networkF e simpler wy to ollet dtset hs een proposed y qross et lF whih uses q informtion from )eet of vehiles t rossrod intersetion SVF o ddress q unertinty @severl meters of errors in the vehile positionAD they preEproessed position mesurements with mpE mthing lgorithmF snformtion from three intersetions were used to trin ndom porest glssi(er to detet the lterl mnoeuvreF en ury of VH7 t up to RH meters from the intersetion entrne ws otinedF hey showed tht model trined using informtion from di'erent intersetions n urtely lssify the lterl mnoeuvre t severl intersetions with similr lyoutsF he longitudinl mnoeuvre is mostly in)uened y the distne from the intersetion nd the stte of the environmentF por exmpleD qri et lF used multiElyer pereptron neurl networkD to predit ehviour mnoeuvre t ontrolled intersetion using s input the tr0 light phse RW SWF his is similr to yielding sitution sine the driver9s ehviour is hnging euse of the signliztionF sn their senrioD it ws oserved tht the vehile only reted to the pssge from green light to red lightF husD the ehviour is highly dependent of the tr0 light stteF elso t tr0 lightEontrolled intersetionD eoude et lF lssi(ed the stopping intention of drivers using w ompleted with fyesin (lter TRD its struture is shown if pigure PFITF pigure PFIT topping intent lssi(er using w nd fyesin pilterD fter TR he fyesin (lter uses the urrent nd pst w output to void sudden hnge of lssi(E tionF heir dtset ontins QHH HHH trjetories reorded t single intersetionF efter lterl mnoeuvre lssi(tionD the longitudinl stop mnoeuvre is lssi(ed using the sme dtset SVF sn this exmpleD the veloity nd elertion were the most importnt fetures nd similr ury to the one for the lterl mnoeuvre ws otinedF he driver ttitude n e found ompring the output of the lssi(tion with di'erent inditorF eoude et lF ompred the output of their lssi(tion with the remining time to stop to (nd nonEomplint drivers @iFeF those tht hve not the intention stopA when lose to tr0 light turning to red TRF hriver ttentionD if mesuredD n e good inditor of his ttitudeF wri et lF reorded the hed position of driver pprohing n intersetion s well s tr0 light sttus nd trjetory TIF he trined rndom forest lssi(er ws le to identify VH7 of unexpeted mnoeuvre up to P seonds efore the mnoeuvre eventF 2.3.2.2 Probabilistic Graphs roilisti grphsD iFeF hynmi fyesin network @hfxA nd ridden wrkov wodel @rwwAD hve een used to infer stte of vriles tht will generte sequene of oservtionsF he ehviour is modelled s hidden vriles tht in)uenes trnsitions of the system to other sttesF rww hs een used to (nd driver9s lterl mnoeuvre TRD TSD TTF hey ll used veloitiesD elertions nd yw rte s oservtionsF por exmpleD n rww for eh of the intended SH 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing route is trined with dt from single mnoeuvre TSF henD proility of elonging to the rww trinedEwith the mnoeuvre is found forD eh of the oserved sequenesF hey mnged to urtely lssify WH7 of drivers lterl mnoeuvresF roweverD they used n unlned dtset @iFeF more driver going stright thn turningA nd oserved tht the understnding of people going stright is hrder due to high vrine in elertions nd veloities oservedF por omprison purposesD eoude et lF used two rwws eh one trined with one of the two ttitudes TRF sn omprison to the w nd fyesin (lter methodD rwws were less urteF snsted of trining from dtsetD priori informtion suh s mps n e used to set models for inferene in fyesin network TUF vefevre et lF extended these models to inlude the vehile kinemtis to detet inoherene etween the expeted ehviour nd the urrent intention of the driver SQD where these di'erenes re used to hrterize riskF he driver9s ttitude is modelled s di'erene etween the intention nd expettion mnoeuvresF urning lights is lso inludedD to understnd inoherent mnoeuvres TVF enother pplition of these models is to otin etter preditions of the future vehile stte TTD TWF roweverD the di0ulty with proilisti grphs is to inlude intertion etween entities without reting loops TTF sntertions re linked to on)it res the likely pth of two vehiles ould overlpsF por eh on)itD priority orders re found using rhEmpF henD the likely longitudinl mnoeuvre is found onsidering pending on)itsF prom the inferred ehviourD tions @elertionsA for eh vehile re found with n dpted sntelligent driving modelF he struture of their hfx is shown in pigure PFIUF SI pigure PFIU hfx struture to infer driver ehviour onsidering intertionF ih squre onE tins stte vriles nd the mnoeuvre of vehileF he mnoeuvre of eh vehiles depends on other vehile sttesD fter TT st enles predition of vehile motion over S seondsF roweverD the slility of these models to sitution inluding numerous element is unknownF wost of the time prtile (lter re used to represent these omplex proilisti distriution SQD TTF st llows n online implementtion tht works with few vehilesD ut performnes ould degrde with sene omplexityF 2.3.3 Discussion he min di'erene etween the two pprohes resides in the length of informtion required to estimte the ehviourF glssi(tion lgorithm hndles lrge feture vetors without inresing the omplexity of the predition phseF fy ontrstD for proilisti grphsD the omplexity will grow with the numer of sttes nd oservtionsF hereforeD lssi(er ould ddress more omplex situtionsF sn proesses where the suession of tion is importntD proilisti grph would e more e0ient s trnsitions re lredy integrted in the lgorithmF sn the next setion out deisionEmkingD proilisti grphs re used to represent sitution trnsitionF glssi(ers ould e used to (nd the urrent ehviour with multiple fetures from the situtionD nd omined with proilisti grph to propgte the e'et of this ehviour on future situtionsF SP por mhine lerningD the trining dtsets re s importnt s the seletion of methods pplied to itF he di0ulty for rod intersetion rossing is to otin su0ient trjetories due to osts s well s su0ient vriety of rossrod intersetion lyouts UHF por exmpleD the dtset used in TR inludes Q million trjetoriesD out of whih only IS thousnd were usedF e less ostly method will e to use q dtsetD however its requires lot of preEproessing SVF purthermoreD the dtset must e lned nd with signi(nt numer of trjetories for eh lssD elseD some mnoeuvre fetures will e ignored resulting in less ury TSF greting the dt set from the point view of the vehile is di0ult euse of mesurement noise nd the di0ulty to oserve omplete trjetoryF he understnding of the lterl mnoeuvre is esierD s fetures like the position of the vehile t multiElne intersetion is highly disriminntF xeverthelessD it will e more di0ult to infer t single lne intersetionsD s hnges to the position nd heding our lose to the intersetion entrneF urning lights re n inditor of the lterl mnoeuvre TVF his is often n unrelile soure of informtion due to its errti usge UIF he longitudinl mnoeuvre set is often restrited to the Stop nd Cross mnoeuvresF st is su0ient for ontrolled intersetion @stop sign or tr0 lightsAF roweverD t unontrolled interE setionsD it ould e insu0ient for the deisionEmkingF por exmpleD the ehviour of vehile with reltively high speed ould e understood s intended Cross mnoeuvre wheres its rel intention is to yieldD thus the sujet vehile will e expeted to ome to stopF he resultE ing sitution will e tht oth vehiles slow down nd ome to stopF sf the yield mnoeuvre would hve een reognizedD the sujet vehile ould hve predited tht su0ient gp will e ville in lose future nd try to ross the intersetionF he ehviour of vehile is di0ult to e inferredF eny pproh disussed ttin more thn WH7 uryF hereforeD it is importnt for the deisionEmking system to reson with errors of the ehviour understnding proess nd drivers9 ttitude tht re importnt soures of preditility unertintyF 2.4 Approaches to Decision-Making at Road Intersection here exist three tegories of deisionEmking for vehile nvigtionX rule sedD proilistiD nd mhine lerningF he di0ulties to tegorize them re the existene of mny hyrid pprohesD di'erent types of output nd systems tht operte on di'erent levelsF ht inds SQ them is tht they ontrol the motion of the vehile using proessed pereption informtion tht hs een enrihed with ehviour understnding or other sitution understnding informtionF 2.4.1 Rule-based uleEsed method models humn knowledge out deisionEmking proess s set of ondiE tions nd deterministi reltionsF hese re lso often lled expert systemF hriving is regulted y rules de(ned y the government nd mostly pplied in strutured environmentF husD this prolem is well dpted to ruleEsed pprohes ut re hllenged y humns unertin eE hviour nd pereption limittionsF he PHHV hee rn ghllenge demonstrted the limited performnes of ruleEsed methE odsF itution tht ould e enountered nd how to resolve them ws stted prior to the hllengeD thus similr ehviour ould e expeted from other prtiipntsF he winner used high level ehviour plnner to stop the vehile t eh intersetion entrne UPF st used mp with ttriutes ssoited to intersetions @illustrted in pigure PFIVA nd vehile position to pln trjetories tht stopped the vehile t the desired positionF o deide whether to enter the intersetionD two estimtors must to e vlidX the preedene nd the lerneF he preedene riterion is vlid when every vehile stopped t the intersetion rrived fter the sujet vehileF he lerne estimtor uilds polygon tht should not e rossed y ny vehile while rossing the intersetion @shown in pigure PFIVAF sf one of the moving entities in the sene will ross this polygon in the time needed to rossD the estimtor will e invlid nd the sujet vehile witsF iven if most of the prtiipnt used similr pprohD some used other methodsF he tem yhsx used the hewx rhiteture to generte their ehviourD tht ws lso pplied suessfully s prt of the grossing emeri projet UQD URF wultiple proesses vote to ontrol di'erent outputs nd n integrtor deides whih to followD the system struture is shown in pigure PFIVF sn the irumstne of the hllengeD rules nd situtions were su0iently simple to e implemented y simple stte mhinesF rowever ollisions hppened during the (nl of the hllengeD with for exmple the ollision etween the ws nd gornell tems PPF st ws used y misunderstnding of the intertion etween the two vehiles omined with one of the vehile to e unexpetedly stoppedF itutions of the rel world re more diverse nd omplex nd would requireD with suh pprohesD more omplited stte mhinesF SR (a) Road network denition le containing the map [75] (b) Intersection view for Boss Team [72] (c) Flow diagram of the Behavior-Based, Winner-Takes-All Driving Behaviors implementation. The behavior Integrator ensures there is one winner from each driver category, after [74] pigure PFIV epplition of ruleEsed methods during the PHHV hee rn ghllenge viu et lF reted stteEmhines for other sitution with tr0s lightD pedestrin nd inluded SS the lne hnge mnoeuvre tht ould e neessry to orretly position the vehile UTF sn their systemD while getting loser to the intersetion entrneD di'erent tions n e doneF ih orresponds to di'erent deelertionD tht for exmple redues the veloityD stops the vehile or performs n emergeny stopF rnsitions re ontrolled y onditions sed on the vehile distne to the intersetion or the presene of other vehiles pprohing the intersetionF he dvntge of using stte mhines is tht the ehviour of the sujet vehile is simple to verify s trnsition onditions re deterministiF roweverD mesurement unertinty ould trigger n undesired trnsition resulting likely hzrd situtionsF ith the multiplition of stte mhines to hndle more senrios nd with unertin informtionD there is possiility to selet wrong stteEmhine leding to inoherent deisionsF o llows for reovery nd ontinuous nlysis of the situtionD two stges ruleEsed deisionE mking system llows for more retivity whilst pprohing the intersetion UUF he (rst stge onsists of etri net tht (lters the set of known mnoeuvres to output only legl nd fesile mnoeuvresF he seond stge uses multiEriteri deisionEmking to evlute mnoeuvres sed onX sfe distnesD distne to the gol nd to drive within lne oundriesF e fst updte of the mnoeuvre hppen with n synhronous event to ret to hnges of situtionD ut the sensiility to trigger these events must e refully tuned to void flse positiveF heveloped originlly for we rwlingD ontologies hve een reently pplied to onsider lrger vriety of entities nd situtionsF en ontology provides mening nd reltionship etween difE ferent elementsF por utonomous vehileD it is dtse tht inludes the entities of the sene nd their possile reltionships UVD UWF he deision is n ttriute of the vehile tht is found y understnding the reltion nd intertion etween ttriutes of the rod nd other entity ttriutesF roweverD the size of the dtse mkes inferene time n issueD lso missing elements my use resoning errorsF o enle relEtime deisionD u'er of surrounding elements is used to otin short response in UWF hese methods lso ssume tht the system hs n high on(dene in the oservtionF yverllD ruleEsed methods hve for dvntge to e deterministiD thus repetle nd simple to nlyseF huring the vlidtion stepD ll senrios ould e nlysed to gin on(dene in the system performnesF roweverD they require roust sitution estimtion to orretly hnge sttesF o use these methods with rel mesurementD lrge mrgins re often reted in order to void entering in situtions where unertinties ould degrde the deision qulityF st results in onservtive ehviour nd sometimes unneessry sfety mnoeuvresF hese methods require sustntil development e'ort if most of the driving situtions need to e modelledF ST 2.4.2 Probabilistic Approach: Partially Observable Markov Decision Process roilisti pproh llows the unertinty to e qunti(ed s proility densitiesF snsted of using (xed vlues to represent n informtionD its rnge nd distriution re lso usedF gonE sequentlyD deisions re hosen s tions tht mximize n expeted rewrdF wodel for proilisti deisionEmking is nmed wrkov heision roess @whAD (rstly deE sried fellmn in IWSU VHF wh onsiders unertinty s stohsti trnsitions fter n tionD ut it needs the urrent stte of the system to e oservle VIF ith this modelD tions tken y the system will 'et oth the system nd its environmentF roweverD most of the timeD the urrent stte of the system is unertinF en extension of this model tht onsiders prtil oservtion of the sitution is nmed rtilly yservle wrkov heision roess @ywhAF sn domins suh s eologyD mrketing nd soil studies this model helped humns to hoose the est poliy nd to estimte its outomes VPF sn rooti pplitionD ywh hs een suessfully pplied on prolems suh s motion plnning VQF e ywh models the deision proess of n gent ting in n unertin environmentF pormlly it is omposed of {S, A, Z, T, R, O}D with S the stteEspeD A set of tions tht the gent n tkeD Z set of oservtions tht n e otined y the gentF T is the trnsition funtion T (x, a) : S × A × S tht desries how the system hnges when the gent tkes tion a when in stte x s the proility P (x |x, a)F he rewrd funtion R(x, a) indites the vlue otined fter performing n tion in given stteF end O(x, a) : Z × A × S estimtes the proility P (z|x, a) to otin n oservtion z ∈ Z eing in stte nd tking n tionF he gent does not know the rel stte fter n oservtionD it resons with elief b ∈ B with b : B → R ≥0 nd x∈S b(x)dx = 1F husD the gol of the gent is to mximize the vlue V : B → R for n initil eliefF sn ywh there exists n optiml poliy π * : B → A tht mximize V F he vlue of poliy n e estimted s the expeted future sum of rewrds for n initil elief

  sn the ontext of e nvigtion systemD the gent is the deisionEmking system tht deides the poliy to e pplied when pprohing towrds the intersetionF he intertions etween elements of the model re shown in pigure pigure PFIW e ywh modelD nd intertions etween the gent @top lyerA nd the environE ment @ottom lyerA woreoverD elements of the ywh model mth the g node internl struture from RR whih llows the ywh to e nlogous to plnner unit of the generi rhiteture in vehile entri systemF imilrities etween these two models re highlighted in pigure PFPHF gompre to other pproh to deisionEmking tht only inlude the ehviour genertion or the vlue judgmentD ywh inludes in its formlism ll the element neessry for deisionEmkingF

2. 4

 4 .3 Machine Learning whine lerning uses dt generted y vehiles to trin models whih re used in the nvigtion systemF he reent vilility of prllel omputing hrdwre nd nnotted dtsets hve rought signi(nt improvements on omputer vision tsks with neurl networkF hese hve lso een pplied to the nvigtion nd deisionEmking prolems with suessF hese models hve lredy een demonstrted in the WH9s with vehile driving on the highwyD solely sed on mers SF here exists three pprohes to mhine lerning tht hve een used for nvigtion purposesX indEtoEindD snverse einforement verning nd einforement verningF indEtoEend lerning lerns the reltionship etween sensors inputs nd vehile tionsF he trining dtset requires imges lelled with drivers9 tionsF fojrski et lF used UP hours dtset to trin gonvolutionl neurl network @gExxA to output steering ommnd QPF hey drove on the highwy nd unpved rod under vrious wether onditionsF e gExx uses TH 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing (lters of di'erent sizes to nlyse the imgeD their outputs is proessed in hidden lyers nd the lst lyer outputs the most likely tion ssoited with the imgeD the struture of the network is shown in pigure PFPIF pigure PFPI indEtoEind neurl networks strutured tht link inputs t the ottom to output t the topD fter QP heir pproh uses single imge to infer the next steering ommndF husD the output of the gExx is retive tion insted of longEterm mnoeuvre plnsF he vlidtion of suh lkEox system is omplexD s weights nd fetures used in the hidden lyers re di0ult to e understood y humnsF einforement lerning @vA nd inverse reinforement lerning @svA re sed on wh ut rely on mhine lerning to uild the rewrdD trnsition nd oservtion funtionsF sv ims to produe humnElike ehviour from demonstrtion mde y expertsF por uE tonomous drivingD it requires driving demonstrtions nd the model lerns the rewrd funtions expeted to explin wht motivted the driverF sv pplied to ehe improves the ssistne y lerning personlized driving style WTF st hs een used to reprodue lne hnge mnoeuvre WU nd to understnd intertion etween drivers on the highwy WVF ewrd models re esier to e lernt in these situtions euse ontexts @lne mrkingsD rod shpeA hve mny resemE lnes ross the rod networkF et rod intersetionsD lerning these models is more di0ultD s there exists vrious lyoutsD intertions nd regultionsF TI 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing einforement lerning nlyses the onsequene of poliy to improve its models nd ontinues this proess until the vlue of poliies stops to improveF his require the system to fil numerous times in order to improveD thus it is di0ult to trin in the rel worldF por utonomous drivingD it hs een pplied to highwy driving WW nd intersetion rossing IHH oth in simulted environmentF wh is used to model n intersetion nd lern the vlue funtion with heep Enetwork IHHF heir gent mnged to lern poliies to move slowly nd to void oluded vehilesF he model hd to try IH HHH times for eh of the four senrios @turn rightGleftD multiElneD strightA to hieve WV7 of suessful rossingF heir method ould e di0ult to trnsfer into rel life onditionsF heir simultor @wyA uses simple kinemti models for vehiles nd driver ehviour tht does not urtely represent the rel worldF sn reent pplition of endEtoEend nd reinforement lerningD it hs een demonstrted tht simultion with some degree of vlidity n e used for trining IHID IHPF hey suessfully drove on rurl nd urn rod with n endEtoEend network trined in simultionF ereived imges re oded into ltent vetor whih is deoded y network trined to generte imges s they ould hve een issued y the simultorF he deision proessD tht hs only een trined in the simulted worldD is le to use these fke imges to generte tion tht re relevnt to the rel situtionF pigure PFPP shows the proess )ow of suh system nd imges used to deideF TP (a) (b) pigure PFPP @Aroess )ow of n endEtoEend system for trining nd deploymentD@A imges used y the network to deide with rel pereived imges @leftA nd the imge trnsferred into the simultion speD fter IHI hen these models re trined with mesurement from rel sensors or rel situtionsD thus onE tining unertintiesD they n e roust ginst similr unertinties ut might underEperform in se the ontext hs hngedF he min reson is tht while trining the model looks for importnt fetures nd ompress the input representtionF husD noises re (ltered in the (rst lyers of models s they re meningless for the proessF hese mhine lerning lgorithms re lso more generlizle s the model serhes for importnt fetures of the sitution tht re present ross ll the sitution of the dtsetF roweverD they re lso prone to overE(tting if the dtset is not lneF por exmpleD t rod intersetion if the model hs een trined using dt t pek tr0D thus ontining mny stop mnoeuvresD the model my predit only stop mnoeuvres even with low tr0 onditionF TQ 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing iven ifD mhine lerning models shows gret performnes on testing dt left out the trining dtsetD their performne on new dt or slightly di'erent dt @hnge ountryD wetherA is di0ult to preditF imultion is regrded s n importnt wy to generte trining dt with ontrolled vrineF ridden lyers mke the nlysis nd understnding of models di0ult tskF he explinility of these models will e n enler for their doption for sfety ritil pplition suh s utonomous driving IHQF 2.4.4 Discussion hen ompred in terms of performnesD generliztionD resoning with unertinty nd exE plinilityD none of the presented pproh outperform the othersF le PFI shows strengths nd weknesses of eh pprohF le PFI trengths nd weknesses of deisionEmking pprohes epproh nertinties uleEsed method provides simple nd understndle models whih tions seletion proess n e understood nd modi(edF roweverD the resoning out unertinties nd intertions is done using pessimisti hypothesisF sf n enountered sitution is not within the prede(ned sope of the system designD ruleEsed methods nnot generte sfe poliiesF roilisti pproh uses unertinty nd intertion models to (nd poliies tht re the est trdeEo' etween performne nd riskF st requires humn knowledge to uild modelsD ut unexpeted situtions re still onsidered s low proility eventsF wethods pplied to solve proilisti deisionEmking prolems online pproximte the optiml poliyD there ould e vrine in pproximte poliiesF husD eh senrio must e tested multiple times to evlution system performne nd stilityF whine lerning models improve on generliztion nd re roust to unertintiesF rowE everD the trining dtset must e lned nd over the vrious tr0 situtionsD these two ojetives re di0ult to hieve longsideF wost of the suessful pprohes use deep TR 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing lerning t some stgeD thus ontin some hidden prmeters tht nnot e understood y humnsF e posteriori prmeter tuning is omplexF st lso 'ets their vlidtion s onditions nd resons for their filure to deide orretly re di0ult to understndF he metris used to evlute these deisionEmking methods re often di'erentF gumulted rewrd is used with ywh nd v to show the performnes of the solver WPF st does not demonstrte tht the hosen poliy is pproprite nd sfeF he numer of ollisions neither indites the qulity of the drivingF v my hieve lose to zero ollisionD s these sttes re highly penlizedF roweverD unexpeted ehviour n e oserved if prt of the environment hs not een explored IHRF ystems tht used their performnes inditors in their vlue estimtion re

3. 2

 2 Functional Discretisation sntersetions re res where di'erent rods onvergeD thus the pth of di'erent entities using the rod network intersets t di'erent pointsF hese re the zones where most of the SHHH juntion relted ftlities our every yer in iurope IVF e typil rossrods intersetion would hve IT potentil ollision points nd V merging points s shown in pigure QFIF he introdution of roundout divided y R these ollision points s shown in pigure QFIF purtherD roundout geometry nd lower visiility urge drivers to slow downD thus reduing the severity of idents PHF TV (a) Crossroad Intersection (b) Roundabout pigure QFI ehile on)it points omprisonD fter PI wo types of zones ontining these points n e disernedX grossing zones nd werging zonesF he former is the result of vehile pths rossing orthogonlly s shown in pigure QFPF he ltter is used y the pths merging to reh the sme exit rnh s shown in pigure QFPF (a) Example of crossing zone (b) Example of merging zone pigure QFP ixmples of the two on)it zone types xvigtion mps re n importnt soure of prior informtion to (nd themF hey store knowledge TW on driving ontextF hey do not represent the mnner humns driveD nor the pprohing veloities s in)uened y the presene of other vehilesF he estimtion of on)it zones with lne entres is inorretF por exmpleD in pigure QFQD ornge nd red polygons represent on)it res estimted with lne entres represented in greenF he pth of driver turning left t the intersetion @in dshed violet strsA does not ross the on)it zonesF his is used y the re within the rossrod tht does not hve lne mrkingsF ht isD the freedom of motion of drivers is iggerF

  tht hd to yield or to stop efore rossingF he motion of driver is 'eted y the presene of other vehiles nd the injuntion not to remin in this rossing reF husD they re most likely to elerteF pinllyD rossing the exiting re ends the mnoeuvre relted to the intersetionF roweverD this re remins importnt s pedestrin rossing or the next intersetion might 'et driver motionF epresenting driver motion t rod intersetion is di0ultF st does not only require to onsider vritions used y di'erent driving styles nd mnoeuvresF xonEprmetri mhine lerning is suited to solve these issues SRD IHTD however these pprohes re omputtionlly expensive s it requires n opertion on the element of the dtset for infereneF en lterntive representtion of these models ould enle more prtil usgesF rhEmps represent di'erent types of informtion stored in series of lyers in geogrphi informtion system @qsA strutureF snformtion inludes rod geometriesD rod ttriutesD s well s informtion from lerning proessF hese mps n e generted using mhine lerning y oserving the struture of the rod IHUF yther mhine lerning pplitions used to unE derstnd drivers9 motion n e nlysed to rete geometriesF hese yEproduts n e stored into the mpF st llows to quikly ess informtion out other driver ehviours sed on spei( lotionF3.2.1 Motion Patterns With Gaussian Processesehile trjetories result from proess tht involves timeF here re di'erent wys to lern models tht represent them IHTD IHVD IHWF qussin roesses re nonEprmetri mhine lerning methods tht emed the dependeny etween time nd vehile poses whilst providE UI ing proilisti pproh for mhine lerningF e motion ptternD modelled with qussin

  where σ n the signl vrine nd l the length sleD they form set of vlues Θ = {σ n , l} lled hyperEprmetersF hese re hnged during the lerning proess to optimise log mrginl likelihoodF e motion pttern n e de(ned s the proility of moving ojet to oupy position t ertin time IHVF UP his proility distriution for moving ojets is not simple to estimteF st n e 'eted y golsD driver9s intertionD driving styleD etF husD lerning sed pproh is often pplied to retrieve this distriution using for exmple gols III or mnoeuvres IHTF rjetories re ontinuous funtionsD lerning these motion ptterns res on regression prolemsF hey re used in rooti pplitions to retrieve the dynmi models of unknown systems IIPF e qussin roess is regression model tht n e pplied when hnges re driven y qussin distriutionsF sn the utomotive dominD they hve een used to represent driver pths IHTDin pigure QFRD nd to nlyse drivers ehviour t stop intersetions SRD in pigure QFRF (a) Path estimations, after [106] (b) Velocity prole regression at stop controlled intersection, after [54] pigure QFR epplition of qussin roesses on vehile motion e hose to trin qs to retrieve the motion pttern of vehilesF gompred to other regression modelsD like support vetor regressionD q regression n represent the unertinty round the predited outputF his llows q to onsider mesurement unertintyF he min drwk of q is the omplexity of the predition step tht grows with the numer of trining smplesF o overome this prolemD trining nd preditions re done o1ineF henD geometries enompssing the predited motion pttern re extrted nd stored in mp to e used t runtimeF UQ 3.2.2 Functional Discretization Framework o (nd on)it nd pprohing zonesD the proposed frmework follows S stepsD s shown in pigure QFS X step ID the frmework uses dtset of trjetories grouped y diretion to trin qs @step PAF henD motion ptterns @step QA re used to determine relevnt zones @step RA tht re then stored in mp @step SAF xextD detils of eh step re presentedF e lled this frmework puntionl hisretistionF he rtionl is tht it divides the ontinuous intersetion spe into disrete res y onsidering driver motion insted of (xed disretistion step tht uniformly divided the speF he resulting disretized zones re stored into the mpF en exmple of the otined zones is shown in pigure QFTD with one rnh segmented into IH zonesF 1. Data Acquisition 2. Gaussian Processes Training 3. Motion Patterns 4. Determine Overlapping Area 4. Determine Approaching Area 5. Map Storage pigure QFS righ level funtion of the frmework withX IA equisition of vehile trjetories into dtsetF PA rining y qussin roesses for eh possile diretionF QA qenertion of the motion ptternsF RA epplition of ptterns to determine the relevnt zonesF SA tore results in mp pigure QFT ones resulting from the disretistion frmework resulting of driver motion pttern pprohing the intersetion from left to rightF he intersetion enter is loted t xaHF 3.2.2.1 Dataset Acquisition e dtset of trjetories is required to trin qsF o overome onstrints in otining nturlisti driving dtsetD simultor ws used to generte the dtsetF gexe is simultor used in the utomotive industry to test funtions of vehiles @dynmis responseD driver monitoringD rwsD etFAF imultion models were uilt to inlude vehile dynmisD tr0 nd intersetion lyoutsF hynmi models used in this simultor onsider the size nd weight UR of the vehileF roweverD it is di0ult to modify the driving style nd to generte edge sesF he genertion of driver ehviour onsiders the intersetion lyout nd the tr0F pigure QFU shows simulted fourEwy intersetionD yield mnoeuvre is imposed to vehiles oming from the leftF sn totl TVH intersetion rossing were simultedF mpling rte ws redued to IH rz to mth known pereption systems nd to void unneessry dt points 1 F mples of trjetories within TH meters from the entre of the intersetion re usedF his distne orresponds to the distne where tr0 signs should e present to wrn drivers out the upoming intersetionF ehile motion strts to e in)uened y the upoming intersetion from this distneF hree vehiles were driven utonomously in the intersetion to generte rndom situtionsF hesevehiles hd di'erent dynmis due to di'erent weights @two vehiles were smll urn vehile nd one hevy fmily vehileAF efter vehile hs rossed the intersetionD it is removed from the simultion nd reppers rndomly in nother rnhF his genertes rndomness for eh rossingD s sometimes vehile with higher priority is lso pprohingF he three vehiles re not lwys present t the sme time t the intersetion to vry the type of intertionsF he veloity of eh vehile is ontrolled y ner tr0 modelF he lterl motion within the intersetion is dynmilly fesileF mples of the reorded trjetories re shown in pigure QFUF st n e oserved tht vehile pths @pigure QFUA nd veloity pro(les @pigure QFUA re diverseF hese di'erenes re produed y models nd mnoeuvres tht hnged t eh rossingF st mkes the dtset suitle for trining s it ontins vrineF he results otined with these simulted dt should e reproduile when q is trined with rel dt s it will e trined with mny di'erent ehviours nd qussin roesses onsider mesurement unertinty dded y rel sensorsF 1 Sampled at 100Hz, the position of vehicles driving at 50km/h would change by 13cm.pigure QFU mple of the dtsetD @A imulted intersetion used to generte the dtsetD @A mples of pths in the dtset with pose vritions due to the model of the vehileD @A veloity pro(les of some vehiles driving in the intersetionD with veloity vritions due to the situtionF @A nd @A show di'erent motions generted y the simultorF mpling time is generlly used s input of qsF roweverD eh trjetory hs di'erent durtionF his orresponds to the ehviours tht dpts the driver motion in response to the situtionF whine lerning is more e0ient when the input sle is the sme for eh trjetory IIQF por exmpleD it tkes less time for rossing mnoeuvres when there is no other UT entities ompred to yield mnoeuvresD s the vehile slows down di'erentlyF he distriution of the mnoeuvre durtion in the dtset shows tht most mnoeuvre durtions re less thn PH seondsD nd n rnge etween IS to QH seondsD s shown in pigure QFVF pigure QFV ristogrm of trjetory durtions in the dtsetF VT7 of trjetories durtion is less thn PH seonds sn the litertureD the distne to the intersetion is often hosen to solve this prolem SRF roweverD this distne is irrelevnt one the vehile enters the intersetion or turns left or rightF o onsider di'erent of trjetory durtionsD normliztion of the time vetor is pplied s followsX

  is the numer of smplesD t i the smpling timeD v durtion of the trjetory nd T i the normlized timeF pigure QFW shows positions nd speed ssoited with two stop mnoeuvres given time in pigure UU QFW nd normlized time in pigure QFWF st n e oserved tht the two trjetories look more similr using normlized timeF of the vehile stte ginst A oservtion in time nd A normlized timeF he top row shows the x positionD middle row shows the y position nd ottom row shows the speed pro(lesF UV snferene of the motion pttern requires position nd orienttion to e used s feture vetorD these re lelledX P osition : x i , y i ∈ R² @QFSA yaw : θ i ∈ {0, 2π} @QFTA ehile positions re expressed in the intersetion referene de(ned in pigure QFIHF sts origin is pled t the entre of the intersetion with xes ligned long the ess rod xisF he vehile referene frme is loted in the enter of the rer xle with the xExis ligned with the diretion of the motion of the vehile nd the yExis points to the leftF st de(nes the vehile positionF he position of the vehile is y onvention the entre of the rer xisF pigure QFIH sntersetion referentilD the position of the vehile is the entre of the rer xis he (nl step on the dtset retion is to group trjetories into N lusters with respet to their origins nd diretionsF por exmpleD N is generlly equl to IP for REwy intersetion with single lne entrnesF his proess uses the (rst nd lst smple of eh trjetory to determine their groupF pigure QFII shows these groups for the dtsetF UW pigure QFII glustered trjetories with respet to their origins nd destintionsD eh olour orresponds to di'erent luster vet D denotes the full dtset to e used for trining withX

pPPP

  f y , f yaw the q ssoited with eh omponent of the motionF ih q is trined with luster of trjetories nd optimized with the minimiztion method de(ned in iqution QFVF he motion pttern n e oserved on twoEdimensionl plne y mrginlizing traj pred,n (T ) with the eqution QFIQF T traj pred (T )dT @QFIQA he motion pttern otined from trjetories of vehile pprohing the intersetion t the entrne tht is ontrolled y the yield sign is shown in pigure QFIPF VI pttern on Ph pln fter mrginliztionF he red olour intensity repreE sents the proility ssoited to eh positionF 3.2.2.3 Discretization of a Merging and Overlapping Trajectory werging nd rossing res n e found where two motion ptterns re likely to overlpF his is found y multiplying the mrginlized motion pttern with eh other mrginlized motion ptternF he merging zones for the motion pttern n nd motion pttern j with 0 < j < N nd n = j isX T traj pred,n (T )dT N j=0 T traj pred,j (T )dT > t cross @QFIRA o onsider the vehile size nd shpeD the resulting zone is geometrilly grown y HFUS metreF his vlue orresponds to hlf of the width of supermini vehileF 3.2.2.4 Discretization of an Approaching Trajectory hrivers go through di'erent sttes while pprohing n intersetion UTF his prt of the frmework identi(es these hnges y oserving vrition of the prole oupny with respet to the previously trined qsF ositions where the mrginlize motion proilities re similr re ssoited with zoneF hree heuristis re used t stop , t slow1 nd t slow2 F ih orresponds to di'erent likely ouption of the speF he zone where the vehile is most likely to stopD is found where the mrginlised motion pttern is higher thn t stop pplyingX T traj pred,n (T ) > t stop @QFISA VP he sme eqution is used for the t slow1 nd t slow2 thresholdsF hey llow for the identi(tion of zones tht re oupied while slowing down to dpt to the intersetion speed or situtionF yverlpping res re ssigned to the highest disretized zoneF hese zones show high men vlueD s over the entire predition timeD they hve een oupied longerF hese zones re grown to onsider vehile size @s with the rossing zonesAF3.2.3 Discretization Resultshe motion of simulted drivers pprohing yield sign t n intersetion is used to show the result otined with the frmeworkF he intersetion is typil rossrods with two streets rossing perpendiulrlyF st orresponds to the motion of driver moving from left to rightD s shown in pigure QFUF rjetories from the lower rnh re used to generte rossing nd merging zonesF pigure QFIQ shows the q preditions fter the lerning proessF he highlighted zone hs high proility of oupnyF st is the onsequene of the yield sign tht fores drivers to slow down or to stop in se nother vehile pprohes the intersetionF pigure QFIQ wp reted with predition from set of qsD the highlighted zone hs high men proility due to the vehile slows downF he thresholds re empirilly found nd the vlues seleted re shown in le QFIF hese vlues hold for ny lusters of trjetories of the dtsetF le QFI hreshold vlues t cross t stop t slow1 t slow2 HFQ HFV HFR HFI VQ he results from iqution QFIR re pplied to de(ne the three zonesD shown in pigure QFIRF hey orrespond to merging @in redA nd rossing @in lueA from vehile pprohing from the lower prt of the intersetion zonesF he size nd shpe of zones depend on the unertinty of the predited motion ptternF ingging the intersetion while one of these zones is oupied my led to ollisionF pigure QFIR flue zones re the rossing zonesD zone in red orresponds to the merging zones he iqution QFIS pplied to the motion ptterns results in the disretized zones shown in pigure QFISF he spe to the left of the intersetion is segmented into four zones @IDPDQDSAF hese zones orrespond to the di'erent sttes of driver9s ehviour while pprohing n intersetionF he fourth zone orresponds to the intersetion nd its exitF his zone hs low proility s drivers tend to quit it rpidly to void ongesting the tr0F of pprohing zonesF he omintion of these steps genertes IH zones s shown in pigure QFITF epplition of these disretistions is mde in the following setionF st is ompred with nother type of disretistion for mnoeuvre lssi(tion tskF VR disretiztion for the proposed mnoeuvre 3.2.4 HD-Map Storage righEde(nitions mps re ore omponent of utonomous vehile systems IIRF hese re uilt from mesurement mde with dedited pltforms equipped with vihe nd highEend lolistion systemsF hese dt re used to (nd the struture of the rod nd di'erent elements of interest in the rod networkF es rods nd stti elements often hngeD mintining these mps is ostlyD lterntive solutions re sought in industry IHPF hese mps re mde of multiple lyers s shown in pigure QFIUF he geometries otined with the funtionl disretistion n e stored in the mp priors lyerF his lyer is uilt trough oservtion of zone over timeF st is generlly used to store tr0 light timingD proilities of enountering prked vehile IIRF he deisionEmking proess uses this informtion to dpt to lol spei(itiesD s two pprently similr intersetions might need to e hndle di'erently due to time of the dy or olusionsD etFF pigure QFIU rhEmp strutureD fter IIR VS qeogrphil snformtion ystem @qsA provide sptil dtse to store nd struture geoE grphi dtF st is often used to uild rhEmpsF ht n e one or multiple pointsD lines or polygons in referentil to whih informtion n e tthedF por exmpleD most rhEwps inlude list of lines ssoited tht represents lne entres to whih re ssoited speed limits nd rod onnetionsF o use these mpsD sptil v queries n e mde using the position of the vehile to retrieve relevnt geometriesF qs lso de(nes list of funtionlities tht must e implemented in mpsF ht inlude oordinte trnsformtionD distne or reltionship etween geometriesD further detils re ville t IISF sn our pplitionD rossing nd merging zonesD tht result from the disretistionD re stored s polygons ssoited to the sh of the rods tht re rossingF husD insted of using the motion ptternD re n e essed with query to the dt seF hile drivingD query sed upon the urrent rod n e mde to retrieve relevnt rossing nd merging resF hile plnning e motionD this zone n e monitored to deide whether or not to rossF epprohing res re stored with their intersetion entrne nd exitF por the ego vehileD they n e used to (nd the orret ple to stop efore rossing or to ontextulize other vehile motions within the other rnh pprohing zonesF 3.3 Manoeuvre Classication por driverD to pln its tionD the omprehension of nother driver mnoeuvre is very importntF e driver9s mnoeuvre is in)uened y ontext @eFgF tr0 signD rod lyoutA nd ehviour of other vehilesF en e n intert with other rod usersD if it understnds their intentionsF heresD humn is le to interpret smll lues of nother humn @iFeF hed nodeD eye onttD etFA s intertion knowledgementsD n e n only oserve vehile motionF hese mke mnoeuvre inferene for mhine very di0ult tskF wnoeuvring t rod intersetion involves tions tht hnge the vehile speed nd hedingF hese mesurements ssoited with ontextul informtion filitte mnoeuvre infereneF nE derstnding intertions is di0ult s ess to every informtion on the sitution is impossileF roweverD lol ontextD inferred from informtion stored in the rhEmpD provides n indition out elements tht 'et driver9s mnoeuvre nd it is simpler to essF fuilding models to nlyse vehile motion requires lrge nturlisti dtset TRF hese re ostlyD s their nnottion requires humn lourF he trined models might over(t on spei( VT rod intersetion where it hs een reordedD ut lso e in)uened y lol or driver popultion spei(tionF o overome these prolemsD simulted environment is often use to gther generi informtion ut often lk of relisti humn ehviour models IHIF 3.3.1 Approachpor the purposes of this workD mhine lerning ws pplied to infer mnoeuvre from drivers9 motion nd ontextF he dtset used to trin models ontins trjetories lels with driver mnoeuvresF hese n e divided into lterl @turn leftD turn rightD strightA nd longitudinl @stopD yieldD rossA mnoeuvresD s de(ned in etion PFQFIF yur lssi(tion pproh ims to retrieve the dependeny etween motion fetures nd these mnoeuvres tht t s lelsF he proposed lssi(er (rst infers the lterl mnoeuvreD then the longitudinl mnoeuvreF he rtionl is tht the lterl mnoeuvre hs n impt on ontext understndingD thus onstrins the motion ssoited with the longitudinlF por exmpleD driver turning left t rossrod needs to slow to dpt its speeds to the urveD wheres going stright the speed is less reduedF gonsequentlyD the sme longitudinl mnoeuvre hs di'erent motion fetures depending on the lterl mnoeuvreF purthermoreD di'erent priority rules re pplied depending on the diretion t the rossrod intersetionF he frmework uses the previously desried funtionl disretistion to get the ontextF por eh reD multiple lssi(ers re trined with fetures from the trjetory dtsetF rtition into zones mens tht the driver motion is ment to e simpler to nlyseD thus performne should inreseF his is demonstrted y ompring the performnes of lssi(ers using these zones nd lssil retngulr onesF enother dvntge of funtionl disretistion is to void the use of positions s feturesF osition is ontinuous nd hnges t eh time stepF st does not provide informtion out the mnoeuvre ut rther on the ontext y ssoition with rhEmpsF sts usge would dd unneessry informtion to the lssi(ersF he proposed frmework follows four stepsD illustrted in pigure QFIVF he (rst step ssoites n extrt of trjetory with one of the zoneD then fetures vetor is uilt from the extrtF st is proessed y lssi(er speilly trined to identify the lterl mnoeuvre in this zoneF henD it is proessed y nother lssi(er trined to reognise the longitudinl mnoeuvreF he output of the frmework is n oservtion of the intended mnoeuvre of driver pprohing n intersetionF VU pigure QFIV he lssi(tion frmework struture used to infer mnoeuvres t rossrod interE setion o generte the required mount of lelled dt while keeping the ost lowD the impt of using simulted dt is studiedF e hyrid dtset ontining oth simulted nd rel trjetories hs een used to trin lssi(ersF hese re mixed in di'erent proportions to study the impt of rel nd simulted dt on the frmework performneF st should e noted tht performnes re only studied y testing ginst rel dtF he rel dtset hs een otined y reording the vehile response from qx plus sw from lolistion system instlled on prototype vehileF por the simultion dtD the sme setup s for funtionl disretistion is usedF en originl ontriution is the use of di'erent lssi(ers for eh zone of the funtionl disretiE stion insted of single one for the entire speF por eh zoneD ndom porest glssi(er @pgA is trined to lssify the lterl mnoeuvre nd two others for the longitudinl mnoeuvreF his is equivlent to uild lrger forestD where eh tree (rst tests the re where the smple ws oservedF he pigure QFIV illustrtes the frmework strutureF his frmework is pplied to the lssi(tion of drivers pprohing n intersetion tht is ontrolled y yield sign on the pprohing rnhF VV 3.3.2 Random Forest Classier he mhine lerning lgorithm retined for this work is the ndom porest glssi(er @pgA IITD whih is metEmhine lerning method tht omines the output of multiple deision trees for lssi(tion or regressionF he prinipl dvntge of pg is simpliity for trining nd testingD it requires simple opertionsF husD they re fst to trin nd to lssify new smplesF rining n e fster using the tree in prllelD s these re independent IIUF gompred to stteEofEtheErt deep neurl networksD pg models re understndle y humnsF st llows for ler understnding of lssi(er deisions nd fetures used to uild testsF st lso requires less prmetriztion s only the mximum depth nd the numer of trees de(ne the struture of the lssi(erF hese re the resons why pg were hosen to lssify drivers9 mnoeuvreF e deision tree is omposed of multiple nodes where simple tests re pplied on the feture vetor uilt from vehile trjetoriesF hese re lernt during trining nd used to guide new smplesto lef node where it get ssigned proility tht elongs to mnoeuvre lssF he output of pg is n verge proility otined s men proility of eh treeF pormllyD tree j n e written s funtion f j (x, α j ) : X -→ Y with X the feture spe nd Y the lss speF α j ptures stohsti elements of the tree @tests for eh deision nodesA nd x is the feture vetorF husD the pg n e written s F = {f 1 , ...f N } with N the numer of trees nd the proility of lss k given x de(ned sXp(k|x) = 1 N N t=1p n (k|x) @QFITA wo prmeters re used to design pgX the numer of trees nd the mximum depth of the deision treeF snresing the numer of trees should led to etter performnesD however osionlly similr trees re dded to the forest whih do not improve the lssi(tionF he deeper tree isD the more it genertes pure nodesD though some rnhes re likely to over(t y reting mny lef nodes tht lssify only single dt pointF he trining phse onsists of (nding the α j for eh treeF o void trees lerning the sme set of testsD eh tree is (tted with di'erent suset of the dtF his methodD known s ootstrp ggregting or ggingD improves the stility nd ury of the pgF ests in tree re found s followsX e group of fetures with di'erent lels is presented t node nd multiple ndidte tests re estlishedF hese re onditions on feture vetors @eFgF VW pigure QFIW ixmple of tree lssi(erD fous is on rnh tht genertes pure nodes @in greenA nd impure nodes @in redAF he (rst row of node is the seleted testD the seond row is the qini sore of the testF (rst feture is less thn vlueAF henD they re ompred in terms of impurity tht would result of splitting the group with the testF e pure node is otined whenD fter splitD group ontining single lel is retedF st lso orresponds to qini sore of HF he qini index is used to mesure impurityF st is de(ned yXGini = i =j p(i)p(j) @QFIUAwith i the orret lss nd j nother lssF his index hs vlue of H when the test n di'erentite ompletely lssF he test with the lowest sore is kept nd used to split the dt into two groupsF henD they re presented to two di'erent nodesF he proedure is repeted until the mximum depth is rehed or pure node is retedF ixmple of tree rnh generted from our dtset is given in pigure QFIWF o infer the lss of new dt smpleD its feture vetor is presented to every tree of the forestF sn eh treeD the feture vetor follows the pth given y the previously lerned testsF hen it rehes pure node or lef nodeD proility is ssigned y the tree to the new smpleF his single predition is verged y the result of ll trees with eqution QFITF WH (a) (b) pigure QFPH tellite view @A nd ground view from the rnh e @A of the intersetionF st n e found t RS°IQ9HPFP4x S°RV9RTFH4i 3.3.3 Dataset por the purpose of this reserhD two dtsets were retedF he (rst is onstruted with rel trjetories reorded from vehile t three rnhes intersetionF he seond is mde of simulted trjetories from digitl representtion of the sme intersetionF 3.3.3.1 Real Environments he experiment ws onduted on threeEwy intersetion in n urn re @qrenoleD prneAF pigure QFPH shows stellite nd ground view of the intersetion used to quire dtF his intersetion ws hosen euse there were no other intersetions in SH meter rngeD thus drivers re not elerting pprohing the intersetion siteF he speed ws limited y the prenh lw to IR mGs @SH kmGhAF he tr0 ws reltively low @no more thn two vehiles interting togetherA nd there were no pedestrinsF he vehile stte ws mesured y highEend lolistion systemsD the Esens @mtiEgEUIHA tht omines n inertil mesurement unit with qxF st mesures the vehile poseD speedD nd elertion t rte of IHHrzF he mesurement is used to represent the trjetory of the vehile from whih fetures re extrtedF hree di'erent drivers ontrolled the vehile for QH minutes to hve di'erent driving styles in the dtsetF sn totl IHI rossings were reordedD with QU in the trgeted rnh @the one with WI the yield signAF his is reltively smll dtsetD further some dt needs to e kept wy for testing purposeF hereforeD the dtset ws enhned with simulted trjetoriesF 3.3.3.2 Simulated Environments imulted trjetories re otined from the intersetion etion QFPFPFIF yne rnh ws deleted to mth the rel intersetionF husD zones otined in the previous setion n e reEusedF sn totlD IHH rossings hve een oserved y driving on the rnh with the yield signF 3.3.3.3 Hybrid Dataset he full dtset is mde of IQU trjetories of driver rossing the yield signF ih trjetory hs een lelled with lterl mnoeuvres @strightD turn rightA nd longitudinl mnoeuvre @stopD yieldD rossAF he lterl mnoeuvre is sed on the (nl oservtion of trjetoryF he longitudinl mnoeuvre lels depends on heuristis on the speed pro(le S 0,1,...,t X min(S 0,1,...,t ) < 0.8m/s → T label = Stop 0.8m/s < min(S 0,1,...,t ) < 3.8m/s → T label = Y ield @QFIVA min(S 0,1,...,t ) > 3.8m/s → T label = Cross e trjetory ws lelled s rossing when drivers kept speed higher thn QFV mGsF hese drivers redued their speed to oey the highwy ode tht sttes tht speed should e redued in the surrounding of rossrodsF hen lower minimum speed ws reordedD nother vehile tht hs the priority ws present thus the mnoeuvre ws lelled s yieldF roweverD it ws oserved tht drivers lowered their speed elow HFV mGs to let multiple vehiles to ross the intersetionF his mnoeuvre is often nmed slipped stop9F husD lower ound ws dded to the yield mnoeuvre to ount for these exeptions nd mke them elong to the stop lssF ixmples of speed pro(les nd their lssi(tion re shown in pigure QFPIF st n e oserved tht there re resemlnes etween syntheti nd rel speed pro(lesF por exmpleD deelertion nd elertion re similrF hese lsses ould e enrihed with pereption system mesurements in order to onsider dynmi ontext nd to rete more lssesF por exmpleD t yield ontrolled intersetionD if nother vehile is oserved in nother rnh of the intersetionD the longitudinl mnoeuvre n e leled s yieldF WP pigure QFPI ixmple of trjetories with their given lelsD dshed urves re syntheti speed pro(lesD plin urves re rel speed pro(lesD horizontl lines orrespond to eh of the heuristis used to identify lssF le QFP htset omposition htset vel ssges gross ield top tright urn right omposition of the dtset is shown in le QFPF his dtset is unlned euse lsses re not present in the sme proportionF e few numers of stop mnoeuvres were reorded in rel lifeF husD simultion n e used to ompenste for this wekness tht would otherwise requires mny oservtion hoursF roweverD it mkes the evlution of the frmework more omplexF o ddress this prolemD kEfold vlidtion is used in etion QFRD tht is the lssi(er is evluted k times with di'erent prts of the dt for trining nd testingF st helps to gurntee tht lssi(er performnes re not dependent on the trining dtF 3.3.3.4 Features petures represent slient hrteristis of trjetories tht n e used to di'erentite lssesF eville mesurements in our dtset re positionsD speedsD heding ngles nd elertionsF ositions were left out of the feture vetorD insted there re used to ssoite smple to zoneF husD the feture vetor ws uilt from the group of mesurements tht elongs to the WQ sme reF o void sensiility to noiseD the extrem of eh mesurement in zone is used s feturesF gonsequentlyD there re T feturesX S max , S min X wximum nd minimum speeds orresponding to extrem in the speed pro(le in m/sF A max , A min X wximum nd minimum elertions orresponding to the extrem of the vehile elertion in m/s 2 F H max , H min X wximum right nd left devition from the men heding ngle in rdinsF o show the vlue of these fetures for the lssi(tionD pigure QFPP shows the feture distriution long the digonl nd pirwise distriutionsF he trining of the pg onsists of (nding α i D tht re onditions on fetures in n utomti mnnerF st n e oserved tht elertion fetures n e used to identify the ross mnoeuvre esilyD s there re two spikes in their distriutionsF roweverD there re some outliers tht needs the ontriutions of other fetures to e orretly lssi(edF por the other mnoeuvresD overlps n e oserved in eh feture distriutionD ut the omintion of two fetures helps to determinte the lssF por exmpleD the omintion of S min nd A max n helps to lssify group of yield trjetory @highlighted y the purple irles in pigure QFPPA with two onditions on these feturesF 3.4 Results o understnd the importne of eh implementtion hosenD omprisons with other pE prohes re mdeF his is three step proessX IF he optimized vlue for the pg prmeters re found y ompring multiple on(gurE tionsF PF gomprison etween retngulr nd funtionl disretistion is mdeD followed y omE prison etween w nd pgF QF euries otined with di'erent dtset proportion re ompred to understnd the impt of simulted dt during the triningF he implementtion is mde using python nd ikit vern for trining nd testing IIVF WR pigure QFPP peture pirwise reltionships in zone P @lue zone of the pigure QFPRAF eres highlighted in purple orrespond to group of yield mnoeuvre tht n e identi(ed omining two feturesF WS 3.4.1 Random Forest Topology he depth nd numer of trees re the two prmeters used to ontrol the retion of the pgF husD it is importnt to (nd their optimized vluesF yutEofEg error @yyfiA is ommonly used to mesure the qulity of the lerning phse of pgF his metri represent the verge error of eh dt point lulted with trees tht did not use the dt point for triningF he lower the vlue of this metri isD the etter the lerning will eF wultiple pg with di'erent on(gurtions hve een trined using dtset ontining PH7 of rel dtF st fvours omprison of the di'erent topologies to (nd where the yyfi is the lowest pigure QFPQ shows the yyfi otined with di'erent sets of prmetersF st n e oserved tht inresing the depth to over IH nodes does not led to signi(nt improvementsF por the numer of treesD the yyfi deresed when PER trees re usedF st implies tht single tree would not e su0ient to lern ehviour ptternsF his prmeter hs lesser impt on the yyfi ompred with the tree depthF st n e oserved tht performnes stilize elow HFP with IH trees nd moreF e on(gurtion hving mximum depth of IH nd PH trees hve een seleted to onstrut pg for the rest of the studyF sn this on(gurtion n yyfi of HFIV is otinedF st orresponds to the re highlighted in green in pigure QFPQF en yyfi sore of HFIR ould hve een otined t the ost of inresing the depth over PS whih inreses omputtion time nd the likeliness of overE(ttingF yne pg hs een trinedD it is possile to ount the numer of times eh feture hs een used to split nodeF st is used to determine eh feture importne nd how muh feture ontriute to lssi(tion tskF le QFQ inludes results otined with the seleted on(gurtionF por the longitudinl mnoeuvreD fetures relted to speeds nd elertions re the most importnt ompred to the heding ngle tht ws seldom usedF V min must hve een used more oftenD s it ws in the lelling proessF st is noted thtD the lelling proess onsiders the glol minimum of the speedsD wheresD the minimum in the feture vetor is the minimum within zoneF he lterl lssi(tion hs more lned usge of fetureF his timeD heding relted fetures hve more importnt thn with the longitudinl lssi(tionF he results show tht eh feture seleted hs n importne for the lssi(tion of the lterl nd longitudinl mnoeuvresF WT pigure QFPQ yyfi error with di'erent prmeters for the rndom forest lssi(ersF he green re represents pg with PH trees nd depth of IH providing HFIV sore @otined from dtset hving PH7 of rel dtA le QFQ petures importne fter trining wnoeuvre type peture S min S max A max A min H max H min of the intersetion spe using the funtionl disretiztionF A etngulr segmenttion @the point HDH is the entre of the intersetionA 3.4.2 Functional Discretization Compared to Rectangular Discretization he performne of the lssi(tion frmework should e improved thnks to the funtionl disretistion presented in etion QFPF o understnd its imptD omprison is mde with retngulr disretistionF he ltter is otined y dividing the pprohing rnh into S zones tht re IH meters long nd PFS meter wideF hese dimensions re similr to the ones used y qross et lF for similr lssi(tion tsk SVF pigure QFPR shows omprison etween the two disretistion methodsF he most left zone of the funtionl disretiztion is ompred to zones R nd S of the retngulr one euse of its width of PH metersF eury ws omputed s the result of the lssi(tion of rel dtD kEfold ross vlidtion with kaQ nd trining mde with dtset ontining PH7 of rel dtF le QFR shows the results otined from eh re @lterl nd longitudinl mnoeuvresAF st n e oserved tht glol improvement of SFR7 for longitudinl mnoeuvre nd SFV7 for lterl detetions ws otinedF WV le QFR gomprison of the lssi(tion ury using the funtionl disretiztion nd retngle tht the funtionl disretiztion is more useful for the lssi(tion tsk ompred to simpli(ed oneF eduing the size of the retngulr disretiztion ould hve led to etter results ut with more lssi(ers to e trinedD thus more omplex systemF 3.4.3 Comparison With Baseline Approach pg dvntges re to e less sensile to noise nd to vrine in the dtF his n e highlighted y mking omprison with seline mhine lerning lgorithmF rereD upport etor whine @wA ws hosenF hey hve een pplied with suess for similr tsks SVD TR nd often served s seline for mny mhine lerning lgorithmsF hue to the multiE lss desription of the longitudinl mnoeuvre detetionD n oneEginstEone pproh is used for w lssi(tionF ht is w is trined for eh pir of lssesF he result lss is hosen y the mximl numer of pirwise wsF por this experimentD hyrid dtset omposed of PH7 of rel dt nd VH7 from syntheti dt ws used with the funtionl disretiztionF le QFS shows the results otined in eh zone with uEfold rossEvlidtion where kaQF por the longitudinl mnoeuvreD the pg performs s good s w in the re lose to the intersetion WW le QFS gomprison etween pg lssi(tion nd w is etter thn w in the two zones furthest from the intersetionF o determine the mnoeuvre lss the furthest from the intersetion is importnt for deisionE mkingF por the lterl mnoeuvreD the pg is over the w in most reF hese results show tht pg is more suitle to lssify mnoeuvres with our pprohF 3.4.4 Results With the Hybrid Dataset he (nl test is on whether or not the use of the hyrid dtset improves the triningF he improvement of the lssi(tion frmework is expeted to improve the more rel dt re usedF he funtionl disretiztion is used nd uEfold vlidtion with kaQ ppliedF he ury sore is otined with rel dtF he evolution of the performnes using di'erent perentge of rel dt in the trining set is shown in pigure QFPSF ith the urrent size of the dtsetD it ws not possile to go over PH7 s not enough rel dt would hve een left for testingF hese results indite tht the more rel dt re used the etter the ury isF ith no rel dt used for the triningD the ury is little over HFS for the longitudinl mnoeuvreF st mens tht the model trin only with simulted dt n reognize some mnoeuvresF husD the two models @rel nd simultedA re not ontrditingF por oth mnoeuvresD the ury inreses when more rel dt re usedF hese shows tht oth the simulted nd rel dt ontriute to the lssi(tion performneF st should e noted tht only smll proportion @here IH7A of rel dt is required to hieve good performneF IHH pigure QFPS eury of the lssi(tion using di'erent omposition of hyrid dtsetD ury is otined with rel dt testing 3.4.5 Discussion esults showed tht the proposed lssi(tion sheme using pgD the funtionl disretiztion nd the hyrid dtset should perform etter thn other omintion of lssi(erD dtset omE position nd disretiztionF o ssess the glol qulities of lssi(erD reeiver opertion urves @ygA re used to ompre performnes hese re shown in pigure QFPTF he top left point @oordinte @HDIAA represents optiml performne with only true positives nd no flse positivesF he loser the urve is to this pointD the etter will e the lssi(tionF erformnes n e determined using the re elow the urveF he steepness of the urve is lso importntD it shows how fst the true positive rte inresesF he urve is otined from verge results of ll the lol lssi(ersF por the multiElss longitudinl mnoeuvre lssi(tionD urves re n verge of the yg for every lssF esultsD in pigure QFPT were hieved on rel mesurementsF st experimentlly shows tht the hosen implementtion is etter thn other pprohesF st lso showsD it is the omintion of the two pprohes tht work est s the use of the funtionl disretistion does not improve the lssi(tion with wsF hese results re otined y ompring the lssi(tion of rel trjetories left out during the trining with their known lelsF he pigure QFPU for di'erent omposition of dtsetF he solution using only simultion dt n still perform etter thn rndom guess @dshed lue lineAD thus informtion from simultion is useful in the lerningF he ddition of ertin perentge of rel dt improves the lssi(tionF st shows tht the informtion provided y dt from simultion helps the lssi(tionF ynly the IHI opertion urves A lterl mnoeuvresY A longitudinl mnoeuvresF hshed lines orrespond to rndom lssi(tionF IHP 3.5 Conclusionhis hpter presented method for mnoeuvre lssi(tion pplying funtionl disretistionF he ltter uses simulted vehile trjetories to trin qussin roessesF hese re used to represent the motion pttern of vehiles t rossrodF feuse of qs omplexityD these motion ptterns re stored within n rhEmp s polygon delimiting the intersetion entrne nd rossing zonesF heir use is shown to improve lssi(tion results @etion QFRFPA he lssi(tion ims to identify the driver mnoeuvre from its trjetoryF e set of ndom porest glssi(ers is trined for eh of the zone of the funtionl disretistionF st retes lssi(ers tht re (tted spei( zone of the intersetionF pgs re trined with hyrid dtset mde of simulted nd rel trjetoriesF foth ontriute to inrese frmework ury s shown with good results otined with PH7 of rel dt @etion QFRFRAF his ontriutes to redue the ost involve of reording nd mintining dtset ontining only rel dtF his frmework enrihes the pereption dt with the driver urrent lterl nd longitudinl mnoeuvresF hese re prt of the oservtion otined y the deisionEmking system out the environmentF unowing the performne of the lssi(er simpli(es its models into the oservtion funtion of the ywhF st is importnt to notie tht this study uses trjetories diretly reorded on the vehile nd not pereived trjetoryF huring the dtset reording in the rel intersetionsD vihe sed pereption from ummelhrd et lF ws lso reorded IIWF he lssi(tion results were unstle with rpid hnges of lssi(tionF he likely use is the di0ulty to estimte the entre of nother vehile from its pereived ounding oxF sn rel onditionsD the ounding ox only overs smll prt of the other vehileD thus the estimted entre is shifted nd hnges s di'erent sides of the other vehile re pereivedF por exmpleD we oserved tht the estimted vehile entre lied in the wrong reD thus the wrong set of lssi(ers were usedF feuse of the omplexity to test the deisionEmking system in the next hpterD simultion is preferredF sn this onditionD the simpli(ed pereption system uses the other vehile entre s oservtionF it llows for the lssi(tion to e usleF e pertinent perspetive is to improve the lssi(tion y modifying the set of pg whilst new rel trjetories re reordedF snsted of retrining the lssi(ersD only suset of trees ould e trined with the new dtF heir ddition to the forest would e onditioned to n improvement of the resultsD espeilly on dt tht were previously mislssi(edF he funtionl disretistion would ene(t for n utomti method to identity thresholdsF fyesin hnge points hs een IHR identi(ed s possile solution IPHF st estimtes when n oserved time relted proess hs undergone hnges in its modelF his ould e pplied on the lerned motion pttern or on trjetoriesF he hyrid dtset ould e improved y dding nother reording modlityX sk humn drivers to drive in the simulted intersetionF his solution hs een used to ompre trjetories generted ontroller nd humns in similr sitution to deide whih is the most vlule IPIF hese trjetories would e lose to rel life trjetories without the urden to reord them t the intersetionF hngerous situtions ould lso e reorded to dd more vrine in the dtset IHS isD the vehile deides wht it is going to do next y onsidering the likely onsequenes tht its tion will hve on the environment nd whether or not it stis(es nvigtion requirementsF he deisionEmking proess is di0ult due to unertinties ssoited with the vehile perepE tion systemD sitution understndingD other driver ehviour nd intertions with other vehilesF purtherD evlution of the vehile tion requires to lne performnes nd riskD tht re often divergent issuesF he ojetive of this hpter is to propose proilisti deisionEmking model pplied to the rod intersetion rossing senrioF his requires understnding the ontext to whih this system is pplied nd how it n e integrted within the vehile rhitetureF o ddress ehviourl unertinty whih is mjor issueD the proposed model integrtes oservtion of the ehviour understnding frmework presented in ghpter PF por this purposeD ghpter R is divided into two setionsF he (rst presents the senrio upon whih the deisionEmking is pplied y desriing the intersetion lyoutD tors involved nd their expeted ehviourF st inludesD the mnner into whih the deisionEmking mehnism n e integrted into typil e system rhitetureF he seond setion presents the deisionE mking frmework modelled s rtilly yservle wrkov heision roess @ywhAF ih element of the model is presentedD emphsis is mde on the spei( design of the rewrd funtion tht uses ehviourl vrilesF he result otined with the proposed solution is presented in ghpter SF4.2 Context and Integration into Vehicle Centric Architectureshe deisionEmking proess di'ers with respet to the pplition senrio nd systems interE ting with itF his setion explins the spei(ities of the studied senrio nd the integrtion of the ssoited deisionEmking into the vehile funtionl rhitetureF 4.2.1 Road Intersection Crossing Scenario he risk ssoited with the rossing of n intersetion hs een explined in etion IFP y looking into identology nd generi deision prolemsF e restrited the fous of this study to the rossEutting senrios involving two vehilesD s shown in pigure RFIF his senrio is IHU pigure RFI grossutting rod intersetion senrio with the two involved vehilesF omplex s the two vehiles re on ollision pthF hrivers n oey or not tr0 signlsD there ould e olusion due to other vehiles stopped t suh intersetionD etF he lyout of the intersetion is like the one used in ghpter QF he two rods ross perpenE diulrly without ostles loking the driver9s sightF st is ssumed thtD there is no other intersetion in SHm rdius to gurntee tht driver ehviour is only 'eted y the studied intersetionF hrivers re wrned out the upoming intersetion nd the priority tht shll e pplied y tr0 signF hree types of tr0 signs re onsideredX stop signD give wy sign or rossrod sign @indites tht the vehile is driving on mjor xis nd hs priorityAF he senrio involves two vehilesX the ujet ehile @A nd the yther ehile @yAF he is the utonomous vehileD equipped vehile entri systems inluding the proposed deisionE mking mehnism to pln its tionsF he y n e either n utonomous vehile or humn drivenD however this informtion is unknown y the F he gol of oth vehiles is to ross the intersetion nd to ontinue on their urrent rodsF sn this senrioD the vehiles re on ollision pthF his n e voided if oth respet the rule indited y their respetive tr0 signs nd tke tions ordinglyF roweverD there is muh unertinty to onsiders in this senrioF pirstD the gets oservtions of the sitution tht does not orrespond to the true stte of the environmentF his is due to unertinty inherently present in its pereption system nd vehile stte estimteF eondD the mnoeuvre intended y the y is not neessry the one expeted y the situtionF gonsequentlyD its motion is di0ult to predit nd to understndF he model presented in etion Q ims to integrte these unertinties in the deisionEmking resoning proessF IHV pigure RFP hi'erent senrios nd ssoited tr0 signs for the rossutting rod intersetion senrio wo groups of senrios n e identi(edX enrio eX the y hs priority over the F he se where the hs to stop due to stop sign is left outF feuse the deisionEmking proess is simpler s it is ssumed tht the shll lwys oey to the stop signF he stop mnoeuvre for the n e prtitioned into two phsesX slowing down to stop nd witing for the y to rossF he deisionEproess does not need to understnd the ehviour of the yD only the oservtion tht the vehile hs rossed is importntF he other senrioD where the hs to yieldD requires to pln tions to slow down to let the other vehile rossD or to stopF sf the y for n unknown reson hs deided to slowdownF his requires understnding the y ehviour nd onsidering unertinty ssoited with the estimtion of the vehile stteF enrio f nd gX they onsider the s hving the highest priorityF he y n e expeted to stop @senrio fA or to yield @senrio gAF sn these senriosD the is expeted to slow down s it pprohes n intersetion without stoppingD s it hs the priorityF roweverD the y might not e ooperting or interting with the nd ould try to ross neverthelessF husD tions of the shll slow it down until it estimtes thtD it is sfe now to ross the intersetionF sn some sesD the might need to stop euse of n unruly driver in the yF he di0ulty for the is tht it needs to lne ll the three mnoeuvres to onsider ehviour unertinty of the yF he proposed deisionEmking proess needs to generte the dequte tions to enle the vehile to ross the intersetion in the est possile mnnerF he three studied senrios hve di'erent solutions depending on the deisionEmking sensitivity to risk nd unertintiesF IHW 4.2.2 Integration of the Probabilistic Decision-Making into the AV Architecture o solve the forementioned senriosD rtilly yservle wrkov heision roess @ywhA is used @FfF uEetion PFRFP for the de(nitionAF st uses ville inputs to generte tion seE quenes @or poliyA nd for their evlutionF henD the solution with the highest vlue is to e ful(lled y nother suEsystemF hese proesses n e integrted following di'erent strutures into the vehile funtionl rhitetureF he hosen struture is presented in this setion together with the rtionle for hoosing itF hree types of inputs re used y the deisionEmking proess for the rossutting senrioF hese re s followsX prom the pereption nd vehile stte estimtion systemsD it reeives oservtions of the other vehile stte nd its ownF hese re sujet to mesurement unertinties @eFgF mesurement noisesD low uryFA rhEmp llows for the ontextulistion of these oservtions with respet to the rod lyE outF he lyout of the rod is otined using highEend tehnologies utomtilly proessed nd mnully hekedF r0 signs nd ssoited priority rules re refully reviewed fE ter the mp retionF husD the unertinty ssoited with the mp is fr lesser thn the one ssoited with the pereption systemF sn our pplitionD no unertinty is onsidered with the rhEmp informtionF prom the sitution understndingD it gets oservtion of the y ehviourF his is the result of pplying the frmework developed in ghpter QF he unertinty on this inforE mtion is estimted from the lssi(tion performnes s estimted in etion QFRF e ywh uses these oservtions to reson with proilisti estimtion of environment true stteF he ywh model desried the sttesD reltion nd rewrds tht orrespond to the senrioF his is used y solver to generte polies π nd to evlute themF oliies re sequenes of tionsF sn theoryD it n (nd single optiml poliy π * F roweverD euse of the lrge numer of stte vriles @FfF uEetion RFQFIA nd the short period of time during whih the estimtion needs to hppenD only n pproximtion of poliy vlues n e otinedF his onstrined the integrtion into the vehile rhiteture s the output of the deisionEmking might vry in unertin situtionsF o ontrol the longitudinl motion of the D the poliy is sequene of elertions @see suEsetion RFQFP for more detils on the tionEspeAF hree solutions re onsidered to interfe the ywh with other nvigtion suEsystemsD these reshown Control system pigure RFQ hi'erent methods to integrte ywh within nvigtion system IA he ywh solver explores nd evlutes polies to pproximte the optiml oneF he result is sent to the motion plnner to onstrut dynmilly fesile trjetory for the ontrol systemF ith few hnges in the sitution or if the sitution progresses s predited y the modelD the poliy holds for some timeF roweverD the pln needs to e updted s the sitution progresses nd new oservtion re quiredF en updte is triggered y unexpeted oservtionsD or y the lk of time to evlute the poliy due to dimensionlity issuesF sn our senrioD this updte might e frequent euse of the lrge numer of sttes nd the unertinty ssoited with the other driver ehviourF PA he ywh solver explores nd evlutes poliiesD ut this time only the est tion for the urrent sitution is usedF his tion is sent diretly to the ontrol system to e ppliedF he hosen tion is the one with the est verge return of ll the poliies resulting from this tionF his method onsiders tht the poliy evlution is di0ult in the long termF husD using one tion is more vlule thn the whole poliyF st is suitle for senrios tht do not require omplex motion mnoeuvresF sn our senrioD the ywh only ontrols the longitudinl motion of the vehile nd does not need to reson over longEtime horizon to evlute single tionF QA he motion plnner genertes poliy sets tht re evluted with the ywhF hese n e gurnteed to e fesile nd to void short term ollisionsF roweverD the optiml poliy is not neessry in the set given y the motion plnnerF he resulting deision might e suEoptiml solution of the deisionEproessD whih ould hve found etter solution if unonstrined y the motion plnnerF he seond option is hosen for this work s the deisionEmking proess only ontrols the III of the proposed systems into ehile entri rhitetureF goloured loks orrespond to the proposed sitution understnding mehnism nd deision mking s prt of the deisionEmking nd nvigtion funtionl lok longitudinl motion of the vehile nd its tions re elertion ommndsF purthermoreD the rewrd funtion uses short term rewrd to del with prolem relted to ehvioursF he vehile entri rhiteture disussed in etion PFPFIFI is dpted to integrte the deisionE mking proessF he sitution understnding system inludes the lssi(tion frmework deE sried in ghpter Q nd ontextulistion tht trnsforms pereption mesurements into oserE vtions used y the deisionEmking proessF he nvigtion system inludes the deisionEmking frmework tht explores ywh poliies nd evlutes them using the model for rod interseE tion rossingF he tionD tht is urrently the est evlutedD is periodilly sent to the ontrol system to e exeutedF he overll rhiteture of the system is shown in pigure RFRF4.3 Decision-Making Frameworkhe ywh frmework needs model of the environment for the solver to (nd the optiml poliyF he model struture is represented y hynmi fyesin xetworkD shown pigure RFSF st shows vriles desriing the stte oth vehiles nd intertions etween their stte vrilesF sn this systemD vriles re disreteF his model is then used y rtilly yservle wonteE grlo lnning @ywgAD tht is n online ywh solver to (nd the est tionF e ywh my inlude ontinuous vriles ut these inrese the prolem omplexityF e ontinuous ywh solvers dds disretistion steps mde in relEtime QHD WPF IIP truture of the ywhD s hynmi fyesin xetwork IIQ 4.3.1 State-Space he stteEspe of vehile is divided in twoX the physil stte nd the ehviourl stteF ehile physil stte is represented y speed nd distne vrilesF he vehile speed s is disretized with step of 1m/sD it rnges etween 0 to 14m/s (50km/h)F he vehile position is trnsformed into the reltive distne of the vehile to the intersetion entrneD thus d represents the distne to the intersetion entrneF st rnges etween 0m to 50m with step of 1mF he funtionl disretistion is not used for this model s steps etween zones re too longF st ws not possile to design tions nd trnsition to work with the funtionl zonesF he ehviourl stte vriles inludeX the expeted longitudinl mnoeuvre e nd the intended longitudinl mnoeuvre iF hese re de(ned over the sme disrete spe of mnoeuvreD s de(ned in iqution @RFIAD pplied to rod intersetion rossing @FfF etion PFQFIAF M ={Stop, Y ield, Cross} @RFIA he intended mnoeuvre represents wht the driver is doingD wheres the expeted mnoeuvre represents wht the sitution requires IPPF his di'erene is importnt in situtions tht inlude humn driver tht is not looking fter ooperting with the F he use my e intentionl @eFgF unruly driversA or unintentionl @eFgF distrted driversAF he ehviour of the is represented only y the expettion vrileF e vehile driven y n utomted system hs no reson to ehve di'erently of wht in expeted y the situtionF pormllyD the stte of the environment is vetor ontining these seven vrilesD iqution @RFPAF hese re shown in pigure RFTF hese vriles indite the lrge spe tht the environmentE stte vetor n tkeF x = [d sv, s sv , e sv , d ov, s ov , e ov , i ov ] @RFPA 4.3.2 Action-Space st desries the mnner how the interts with its immedite environmentF sn our pplitionD the vehile hnges its speedF his is done y modifying the elertion of the vehileF husD IIR pigure RFT riles de(ned for the model pplied to rod intersetion rossingF the tionEspe n e regrded s n ensemle of possile elertionsF hese llow the to stop t the intersetion strting from its urrent speedD nd to elerteD if it infers fvourle situtionF he genertion of strong deelertions to void ollisions @iFeF sfety rkeA re not inluded in the modelF he deisionEmking system ims to redue the risk of ollision y tking preEemptive tionF he ontrol system oeys to the ommnds nd ensures tht the vehile mnoeuvres within the sfe dynmi pilities of the vehileF e ontinuous tionEspe is possileD however it dds unneessry omplexity y disretising the tionEspe onlineF hus most of the work use disrete tionEspes with Q to S tions WID QHF roweverD the resulting ommnd is not smoothD thus the ontrol system needs to ompenste thisD model preditive ontrol is one of the solution dpted to this prolem IPQF sn view of these oservtionsD the tionEspe is de(ned yX A ={-2, -1.5, -1, -0.5, 0, +1}m/s 2 o pproh rod intersetionD vehile most likely hs to deelerteD thus the tionEspe ontins wider olletion of deelertionsF e 0.5 m/s 2 step should e su0ient to redue unomfortle elertion hngesF e single tion mkes the vehile to elerteF his is for situtions where the vehile restrt fter stopD to slowly elerte fter the y hs rossed or to inrese the speed little when oth vehiles re interting orretlyF hese tionsD sent to the vehile ontrol systemD mke the vehile hnge its speedsF hey re pplied during ∆t = 0.5sF IIS d sv d sv s sv s sv a pigure RFU rnsition of the physil stte of the F he size of the tionEspe is dpted to the pilities of our solver tht (rst estimtes the vlue of eh tion @FfF etion RFRA nd ould under perform with more tionsF 4.3.3 Transition Model he trnsition model desries how n tion of the hnges the environment stteF he trnsition funtion is given yX P (x |x, a) =P (s sv |a, s sv )P (d sv |a, d sv , s sv )P (e sv |s sv , s ov , d sv , d ov ) @RFQA P (e ov |s sv , s ov , d sv , d ov )P (i ov |e ov , i ov )P (s ov |s ov , i ov )P (d ov |d ov , s ov ) he (rst prt of the trnsition model represents the impt of tion of the on its own physil stteD s shown in pigure RFUF he orse disretistion nd the reltively longEtime step @HFS seondsA mkes omplex ontrol nd physil models of the vehile unneessryF enyhowD n unertinty on its pility to exeute n tion is onsideredF st is produed y instility in the ontrol or externl ftor @eFgF wet rodAF hese re modelled s qussin distriutions over the hnge of speed nd distne s de(ned in iqutions @RFRA nd @RFSAX IIT pigure RFV ixpettion trnsitions for the nd the yF hese depend on their physil sttesF P (s sv |a, s sv ) = N (µ s , σ a ) @RFRA W ith µ s = s sv + a∆t σ a = 1 P (d sv |a, d sv , s sv ) = N (µ d , σ a ) @RFSA W ith µ d = d sv -(s sv ∆t + a0.5∆t 2 ) o infer the expettion trnsitionD the in)uene of the priority rule in the vehile expeted mnoeuvre is onsideredF his stte vrile ould e (xed to e the sme s the priority ruleF roweverD when pprohing n intersetionD it is sometimes di0ult to infer wht driver should doF efter pplying n tionD there is unertinty out wht might hppen nextD it is therefore importnt to model lso the expettion trnsition s proilisti trnsitionF ynly the sitution fing stop sign is unmiguous s there is ertinty on the expettion tht the vehile in nominl onditions will exeute stop mnoeuvreF he trnsition is the sme for oth vehiles nd depends on their physil sttesD s shown in pigure RFVF IIU e used gp eptne model from olltshek et lF to estimte the proility tht vehile stops for given time gp IPRD s expressed in iqution @RFTAF hen the gp is smllD the proility to stop is highD whilst for lrge gpD the proility will e smllF he gp eptne model hs een dpted to inlude the yield mnoeuvreF rmeters δ nd σ re respetively set to TFI nd ER s in vefevre et l SQF p stop = 1pass = 1p stop @RFUA hependene on the priority rule implies three sesX IF ping stop signD the only legl nd expeted mnoeuvre will e to stopD thus the trnE sition proility will e given y iqution @RFVAF P stop (e v1 |s v1 , s v1 , d v2 , d v2 ) (e v1 = stop) = 1 P (e v1 = yield) = 0 P (e v1 = cross) = 0 @RFVA PF ping yield signD driver is expeted either to stop if the gp is smll or to slow down nd yieldF husD iqution @RFTA gives the proility for the vehile to ome to stop with its omplement representing the proility to yieldF sn this se the trnsition proility is s followsX P yield (e v1 |s v1 , s v1 , d v2 , d v2 ) (e v1 = stop) = p stop P (e v1 = yield) = p pass P (e v1 = cross) = 0 @RFWA QF sf the vehile is given the priorityD ll the three mnoeuvres re expetedF he rossing mnoeuvre is given slightly higher proility to represent the ft tht this vehile hs the priorityF he driver is expeted to yield or to stopD if the gp is too smllF his trnsition proility is s followX IIV e ov i ov i ov pigure RFW sntention trnsition for the yF st depends on its ehviourl stte P cross (e v1 |s v1 , s v1 , d v2 , d v2 ) (e v1 = stop) = p stop P (e v1 = yield) = ppass 3 P (e v1 = cross) = 2 * ppass 3 @RFIHA his model is pplied to oth e sv nd e ov F he intention trnsition proility depends on the previous intention nd the expettion of the yD s shown in pigure RFWF his trnsition uses the notion of intention ontinution SQF ht isD if the urrent driver9s intention is the sme s its expettionD the driver9s intention is likely to remin the smeF e driverD tht hs the orret intentionD is likely to follow it in the futureF ytherwiseD if i ov nd e ov re di'erentD the next intention is unknownD therefore it will e uniformly distriuted over the mnoeuvre setF his implies tht the driver hs n errti ehviourF his n e represented y the iqution @RFIIA P (i ov |e ov , i ov )    P (i ov = i ov ) = 0.9 i ov = e ov P (i ov ) = U(I) otherwise @RFIIA he (nl prt of the trnsition model onerns the physil sttes of the y tht depends on its previous stte nd ehviourD s shown in pigure RFIHF es for the with iqutions @RFRAD these trnsitions use simple kinemti models to (nd the distriution of the next s ov nd d ov F he tion of the y vehile is unknown ut mostly depends on its intentionF por eh mnoeuvreD di'erent elertion distriution is used to predit the future speedD iqution @RFIRAF he next distne is found with these speeds nd previous distneD iqution @RFIQAF IIW i ov s ov d ov d ov s ov pigure RFIH hysil trnsitions for the yF hese depend on the y intention nd previous physil stte P (s ov |s ov , i ov ) = s ov + N (µ acc , σ acc )∆t @RFIPA P (d ov |s ov , d ov ) = d ov + s ov * ∆t + 0.5 * N (µ acc , σ acc )∆t 2 used to predit the proility of future environment sttesF here re severl vriles nd unertinty out the e'et tions of the on the environmentD thus it eomes di0ult to keep meningful distriution over eh vrile fter few stepsF sn this pplitionD the trnsitions re sed on expert knowledge nd simple kinemti modelsF eentlyD mhine lerning hs een used to model the trnsition funtion IPSF st provides etter models for the trnsitionsD howeverD it lks explinility introduing prolems into lredy omplex proessF hese models re lerned using oservtions nd not on the true stte of the environmentF wodels for the y motion @in iqution @RFIPAA nd the intention model @in iqution @RFIIAAn e repled y suh methods to etter over humn ehvioursF he pproh of ewon et lF for exmple ould e used to omine mhine lerning with gp eptne models to mintin the understndility IPTF IPH 4.3.4 Observation-Space he oservtionEspe desries informtion otined y the gent fter n itertionF he elief over the true stte of the environment is estimted from these oservtionsF hey re otined from the exteroeptive nd proprioeptive pereption systems nd sitution understnding sysE temsF he oservtion vetor ontins informtion out the nd the yX z = [z sv , z ov ] @RFITA with z sv the vetor of oservtion nd z ov the y oservtion vetorF

4.3. 5

 5 Observation Model his model hrterizes the proilisti reltion etween oservtions nd the environment stteF es eh omponent of the oservtionEspe hs its ounterprt in the stteEspeD it n IPI e desried yX P (z|x) = P ( dsv |d sv )P ( ŝ sv |s sv )P ( dov |d ov )P (ŝ ov |s ov )P ( îov |i ov ) @RFIWA his prt of the model is the most omplex to uild for this type of pplitionF he unertinty ssoited with the sensing hin is di0ult to hrterizeF he pereption systemD tht gives d ov , ŝov D hs di'erent type of unertinty for eh sensor used @eFgF mer glreD lost lidr pointsAF hen s dt gets proessedD environment sensing unertinty hngesF hen multiEsensor dt is fusedD unertinty is redued through the fusion proessD neverthelessD the distriution representing the environment sensing unertinty remins omplex to modelF he sme pplies to oservtions of the sttesD they re 'eted y sensors unertinty nd lol phenomen tht 'et q lolistionF st 'ets how orretly the stte of the is inferredF gommunition etween vehiles provides more urte oservtionsD howeverD it introdues other types of unertinty @eFgF trnsmission delysAF sn simultionD the physil stte n e known without unertinty y essing the stte of eh gentF o lne etween omplex oservtion model required for relElife implementtion nd low unertinty hievle in simulted environmentD the oservtion model ssumes tht eh oE servtion follows qussin distriutionF yservtion of the physil stte re modelled with qussin noise inluding smll vrine N (0, 0.5)F imilrlyD qussin noise is onsidered for the y oservtionsD howeverD we hve inresed the vrine N (0, 1)F he inrese of the vrine represent the ft tht the unertinty from the pereption system is higher thn the veE hile stte estimtion systemF hese qussin noises re good trdeEo' neither to overestimte nor to underestimte unertintiesF he oservtion model of the intention uses performnes of the lssier introdued in setion QFR to know the proility of the lssi(er returning the orret estimted mnoeuvreF he ury is estimted t HFVF gonsequently the true intention of the y hs proility to e orret of HFV or to e uniformly wrong otherwiseF his is desried in iqution @RFPHAF P ( îov |i ov ) he rewrd funtion pplied to rod intersetion rossing use terminl rewrd models QHD WPF hey generte lrge penlty for ollisions nd high rewrd for rossing the intersetionF ystems using these simple rewrd funtion disply high performnes in terms of rossing time nd ollision voidneF roweverD they do not onsider the ehviourl intertion with other drivers while pprohing the intersetionF his intertion llows to solve the sitution in mnner tht oth vehiles re stisfying their intentionF xeverthelessD these must e used refully s some humn driver might not intert with the @see etion IFPAF he ewrd funtion proposed in this thesis omines short term nd ehviour relted rewrdsF his is de(ned in iqution @RFPIAF R(x, a) = w c (d sv )r c (a) + w r (d sv )r r (s sv , d sv , s ov , d ov ) + w e (d sv )r e (e ov , i ov ) + w s (d sv )r s (s sv , d sv a) @RFPIA + w i (d sv )r i (e sv , i ov ) ih rewrd omponent is weighted y liner funtion w(d sv ) = k 1 d sv + k 2 F st depends on the distne to the intersetion entrne to lne di'erently eh rewrd omponent s the pprohesF por exmpleD omfort nd intertion re importnt fr wy from the intersetion whilst onsidering ollisions risk t the intersetion is not of onernF hese onerns hnge when the is lose to the intersetion entrneD risk must e prevented even if pssenger omfort is ompromisedF ith this pprohD the rnge of eh modlity must e the smeF ytherwiseD the weight hs few or no e'et on the glol rewrdF por this purposeD we introdue vrile R max suh tht -R max < r(s, a) < R max F hese weights re found y iterting over possile on(gurtions to (nd the pproprite oneF st is expeted tht mny on(gurtions re vile nd will llow for personlistion of the ehviourF he (rst prt of the rewrd funtion uses physil vriles nd the tions to ompute three types of rewrdF The comfort reward r c (a) penlises tions of the gent with strong deelertionF hese re penlised to void pssenger disomfort in the vehile IPUD IPVF hereforeD in our setupD the gent is only penlisedD if it hooses the strongest deelertionF ytherwiseD the system gets no rewrdF IPQ r c (a)

  he seond prt of the rewrd model is mde of two omponents tht use the ehviour vriles IPR

  ha) @RFQHA IQH ha is the urrent history otined y pplying tion aD C is prmeter tht ontrols the trdeE o' etween exploittion nd explortionF sf C → 0D exploittion is fvoured s only the vlue funtion impts the gfID thus only the tion tht hs the most vlue is triedF snresing CD in n order of mgnitude su0ient to outweigh the vlue funtionD mkes the system exploring eh tion uniformlyF hi'erent vlues for this prmeter will e tested to (nd the optiml on(gurtion in etion SFRFIF etions with the highest vlue re hosen more oftenD ut s the estimtion of the vlue funtion onvergesD new tions re tried to re(ne the estimtion or to (nd void lol minimum of the vlue funtionF 4.4.3 POMCP Algorithm he ywg joins the gfI nd wg to solve ywhs in n online mnnerD tht is ertin mount of time is given to estimte the vlue funtionD then the poliy tht mximise this estimtion is pplied y the gentF ith this step y step pprohD only the strting tion of the poliy is used s ontrol inputsF he ywg lgorithm is divided into three proeduresX erhD imulte nd olloutF he following explins these proeduresF he search proedure strts the lgorithmF st smples prtiles x from the urrent eliefD or from n initil distriution I if the history is emptyF his prtile is used y the stimulte proedure to updte the lue funtionF yne ∆t elpses in the rel lifeD the smpling stops nd the tion tht mximises the vlue funtion is returned to e exeutedF his repeted until the gent hs (nished its missionF he elgorithm is in RFIRF he simulate proedure updtes the stte of the prtile using the genertive model nd uilds the wg treeF he nme simulte is used euse eh prtile represents one the possile future tried using the model tht represent simpler environmentD efore eing pplied in the rel worldF his is reurrent proedure tht is stopped when γ depth < F he simulte proedure serhes if the tree ontins node ssoited with the history of the prtileF sf notD it retes new node for eh tion nd pplies the rollout proedureF hen the history is lredy in the treeD the gfI is pplied to hoose n tionF st llows to uild upon previous evlution to fsten the evlutionF his tion is pplied on the prtile nd the proedure restrts with the updted prtileF yne the reurrene hs stoppedD nodes of IQI pigure RFIR erh roedure the tree rehed y the simulte proedure re updtedF he node vlue is updted with the umulted disounted rewrd of future episodeF he full proedure is in elgorithm RFISF he rollout proedure ims to quikly explore sttes nd tions when node is retedF st pplies defult poliy tht shll resemle high vlue poliyF he two ommon proedures re the rndom tion seletion or tion ontinutionF he rndom tion rollout poliy rndomly selets t eh episode n tionD wheres tion ontinution repets the lst tion over nd overF sn our senrioD the optiml poliy is more likely to ontins sequene with the sme tion ppliedD to slowdown for exmpleF husD the tion ontinution rollout poliy is usedF he disounted umulted rewrd is used to initite the vlue funtion of the orresponding historyF he serh is stopped fter ∆t hs pssedF henD the tion tht mximise V (ha) is hosen nd sent to the ontroller for the durtion ∆tF xodes tht do not ontin the new history re pruned from the treeF he proess is repeted until the vehile rosses the intersetionF he more the serh proedure is repeted the more urte the estimtion of the vlue funtions getsF he omplexity of the modelD the time horizon nd the poliy explortion re prmeters tht 'et the time it tkes to omplete the simulte proedureF here is lso trde to do etween the evlution performnes nd these prmeters @see etion SFRFIAF he simplest implementtion of the solver is used for in our systemF e more dvned implementtion n inlude imittion lerning to initite V (ha) with knowledge otined y looking t humn driversF IQP pigure RFIS imultion proedure pigure RFIT ollout proedure IQQ 4 Probabilistic Decision-Making, Applied to Crossroad Intersections 4.5 Conclusion e frmework for n utonomous vehile to deide how to pproh nd to ross n intersetion is presented in this ghpterF he pproh is sed on ywh to reson onsidering the unertinties ssoited with the sitution nd driver ehviourF sn order to solve the proposed ywhD n online solverD ywgD is used to pproximte the poliy vlue during exeutionF ghpter S presents the pplitions of the frmework s well s the mens developed for its evlutionF he ywh ontrols the longitudinl motion of the sujet vehile y hoosing tions tht re evluted onsidering the ehviour of the other vehileF he tries to intert with the y y tking tions tht llow intertionsF st works y inluding ehviourl vriles into the ywh rewrd modelF he multiEojetive rewrd funtion llows for the ustomiztion of the gent ehviour to fvour ertin rewrds with respet to the intersetion entrne distneF sn order to use this omplex model n online ywh solverD lled ywgD is ppliedF his estimtes the vlue funtion of poliy rnge y lning explortion nd exploittionF o integrte the frmework into the utomted vehile rhitetureD only the next est tion is used nd sent to vehile ontrolF st voids plnning vehile motion for longEtime horizon wheres hnges re likely to our oftenF roweverD this results on the vehile deision to e nonEdeterministi s muh rndomistion is involved in the ywgF gonsequentlyD given the sme initil onditionD the poliy of might e di'erentF his is to e expeted for driving pplition s oservtion nd intertions re unertinF he proposed models rely minly on expert knowledge of the senrioD some prts of this model ould e improved y using mhine lerningD however this is not trivilF o ddress mesurement unertintyD it is neessry to know models of the pereption systemF roweverD these re often unknown or too omplited for use during the explortion phseF purtherD the solver strts from lnk stte t eh exeutionF e memory ould e used to initilize the vlue funtion from previous explortionD elerting the serh for the optiml poliyF his initilistion ould e lerned from oservtion of humn ehviourF he presented model onsiders only one other vehileF o sle the modelD two prt of the frmework need to e improvedF pirstD the desription of the model should inlude intertion mutul intertion tht eh vehile hs with eh otherF st ould rete interdependeny etween the ehviour of eh vehile IQRF he seond prolem is the solver performneF fy dding more vehile to the environmentD the stte omplexity inreses exponentillyF e solution is to IQR rek down the prolem into single ywh for eh gent nd the sujet vehile to solve eh prolem in prllelD ut then intertions re di0ult to onsider nd the hosen tion likely suEoptiml for most of the sene gent IQSF enother solution would e to use utoEenoder to model the environment IQTD ut this model lks of understndilityF IQS 5.1 Introduction xvigtion is omplex tsk due to the rndomness in the ehviour of the di'erent entities shring the sme rod networkF wultiple situtions emerge tht require the vehile to dpt its ehviourF por this purposeD it is neessry to hve digitl representtion of the world through the quisition of dt from multiple sensorsF his is then used to reson nd to determine the vehile ehviour efore given situtionF pinllyD the ommnd signls re generted to IQT tute the vehile with the vehile ontrol systemF hese re utomted yerEphysil systems @egAD nd if they re to operte utonomouslyD need to e sfe nd to e epted y soietyF o evlute the deisionEmking frmework proposed in ghpter RD sttistil rgument needs to e uilt in di'erent senriosF his is prt of the vlidtion proess IHQF his hs proven to e more di0ult thn expetedF pield experiments re resoure intensiveD there re multiple senrios ut fundmentlly multiple hzrdous situtions would emergeF he use of simultion

  proedures nd ssoited dvntges nd drwks rodsF imultion is regrded s mens to ensure the system n operte s expetedF purtherD it llows to test in di'erent hzrdous onditions eyond wht ould e possile physillyF imultors re lredy deployed in the utomotive industry to test di'erent hrteristis of vehileF por exmpleD it is used to understnd the intertion etween driver nd the vehile in QTH view ngle simultor or to test ehe funtions IRPF here re urrently three methods to test e systemsX simultionD losed test trks or (eld opertionl tests in puli rodsF o uild the sttistil rgumentD severl kilometres must e drivenF st is estimted tht illions of hours re neessry onsidering tht some dversril onditions re enountered IRQF his is ostlyD thus impossile for universitiesD wis or strtE upsF iven if simultors hve high initil investment ostD the ost of test is insigni(nt while repetly is high nd physil hzrd voidedF roweverD (delity depends on the representtivity of the model with respet to relityF

  inspetionF he veri(tion nd vlidtion dt mngement onsist of systems pplied to nlyse results from the test frmework @test mngement nd the test exeution pltformA nd to ontrol the senrio explortionF he nlysis inludes tlogue of uss uilt to exmine the in spei( group of senriosF st inludes systems to uild senrio from rel world tr0 dt se nd systems to instntite the eg with di'erent on(gurtions to e testedF IRH pigure SFI he three stges of the inleEsQ vlidtion nd veri(tion rhitetureD fter IRRF he eg ontins the system under testF hesis ontriution to the ixefviEQ rhiteture re highlighted IRI he work in this thesis ontriuted to the us tlogue nd the formultion the testing frmeE work tht runs tests required y the vlidtion mngementF yther systems were either impleE mented in simple mnner or ignoredF es prt of joint work with the ews tem from snri ennesD lueprint to nlyse results otined y pplying ttistil wodel gheking is done nd presented in etion SFRFRF pigure SFI highlights the di'erent ontriution of this thesis to the inleEsQ rhitetureF he testing pltform exluded the infrstrutureD ommunitionD nd opertorD euse the is developed for vehile entri systemF hilstD it is impertive to test ll vehile funtions s muh s it is sttistilly redileD this is mjor endevour nd still in explortory phseF o nrrow the sopeD other utonomous driving funtions suh s lolistionD pereptionD ontrol etF re inluded in funtionl levelD tht isD they operte nominllyF xeverthelessD these simple models dd qussin noise to environment oservtions to reprodue in simple mnner mesurement unertintiesF e more dvned sensor model needs to inlude flse detetion nd missed detetion to urtely represent issues enountered in our (eld trils IRSF imultion is performed using the ner 2 pltformD orporte tool t enult used for the vlidtion of ehe pplitions nd di'erent e funtionsF ner hs the pility to repreE sent rod networksD generte tr0D simulte sensors nd the dynmi modelF por our test se genertionD uses mp representing one of enult9s test sites tht resemles the intersetion studied in ghpter Q is usedF his ws dpted from lne level mp of the test siteF yn this mpD the tr0 signlD the y nd the n e pled to uild templte for the senrio genertion systemF he test mngement system relies on ner simultion softwre funtions to mnge the difE ferent modules required y the testF st restrts the simultion fter given mount of time or fter n event hppenedF he senrios re exeuted in sequentil mnnerF efter the exeution is ompletedD mesured dt re proessed y using python sripts to extrt the uss relevnt to the senrioF yther funtions of the lidtion nd eri(tion mngement were kept to minimumF por exE mpleD the senrio genertion rndomly explores the senrio speF st uses senrio templte from the test se genertion nd hnges its initil prmetersF hese reX the initil vehile veloityD the initil distne to the intersetion nd the signlistionF ghnging these vlues is su0ient to modify intertions hppening while pprohing the intersetionF he us tlogue is onstruted from the highwy ode nd wht ould e onsidered s n exemplry ehviour for rod intersetion rossing @FfF SFQAF he result nlysis identi(es exeutions where the eg filed to reh the performne required y usF his is improved y pplying wg to vry the performne threshold to otin more detiled understnding of system performneF he min dvntge of the inleEQ rhiteture is tht it n e pplied to di'erent types of egF husD n using di'erent pprohD suh s rule sed or mhine lerningD n e tested in the sme frmework to ompre resultsF por exmpleD in etion SFRD the deisionE mking proess is ompred with the one use in the simultor to generte driver ehviourD with the sme testing rhitetureF pigure SFP summrised the rhiteture of our system in intertion with simultion environE mentF he environment simultion models the y ehviour nd the dynmis response to n tionF his informtion is shred into the simulted world for the y to intert with the F en interfe shres the oserved stte of the simulted world with the vehile intelligeneF he vehile intelligene min purpose is to inlude the D tht is the deisionEmkingF st proesses nd ontextulises oservtions from the environment y using the nvigtion mpF he ehviour understnding systemD proposed in ghpter QD is used to enhne the oservtion with the y intentionF he genertes elertion ommnds tht re sent to the dynmi model of in the simultor vi the ridgeF he runs on vinux mhine under the ooti yperting ystem @yA middle wreF st ommunites vi n h ridge with the simultion tools ner on gF his rhiteture needs for their time se @lokA to e synhronisedF y n reeive lok signl nd synE hronise with itD thus the ner lok is used s the mster lok for oth systemsF e detiled view of the di'erent elements of oth the simultion nd the is shown in pigure SFPF he simultion sends the list of ll the entities in the seneF his is ontextulised with the rhE mp to (lter non pertinent entities sed on their distne from the sujet vehileF henD the mnoeuvre lssi(tion is performed to get the intention oservtion nd oservtion ssoited with eh vehile re ompleted with vriles orresponding to the oservtionEspe de(ned in etion RFQFSF henD the deisionEmking proess uses these oservtions to return the likely most vlule tionF st is trnsformed to orrespond with the interfe for the dynmi model of the vehile prt of the simultion to updte the stte for the next time stepF hen the simultion world IRQ pigure SFP oftwre rhiteture of the developed systemF he environment simultionD inside the red retngleD is uilt with nerF st updtes the environment with respet to the tions nd returns the stte of the environment s oservtionsF snside the lue retngleD running under the y middle wreD oservtions re proessed nd used y the deisionEmking proess to (nd the most vlule tionF his is trnsformed to mth the interfe with the simultionF IRR is updted with the new stte of y nd the proess is repeted until the simultion run ends fter PH seondsF prom this testing frmeworkD muh dt is generted nd used to ompute the uss 5.3 Key Performance Indicators to Evaluate Decision-Making System he preferred metri to evlute n eutomted ehile is the wiles per hisenggementD whih is the only metri required to e pulily ommunited y lw to the heprtment of wotor ehiles in gliforni @hwAF his reports the verge distne vehiles operte without humn interventionF st is only n inditor of the progress mdeF esting es is very ostlyD the prototypes re expensive likely ove the ISHFHHH iurosF he logistis required re very highF st requires skilled sfety driversD support engineersD sfety o0ersD n infrstruture to store reordingD nd to nlyse dtD etF hereforeD this metri is unfesile for reserhersF st lso implies driving in puli rods whih is hzrdous nd dministrtively omplexF here re three types of metris used to evlute deisionEmking proessesX to re)et the inE trinsi system performneD for exmple ywh re often evluted in terms of the umulted rewrd ross n exeutionF o oserve simple events tht should not hppen during opertionD for exmple tht no ollision ourred during the testF pinllyD metri mde to ompre vrious lterntivesF he evlution of proilisti deisionEmking system in n utomotive ontext requires to go eyond these metris to etter understnd the systemF his is lso required for mhine lerning pprohes @FfF etion PFRFQAD they re fundmentlly sed on ywhF he min omplexity ftors reX IF e deisionEmking proess sed on unertin informtion n only e nonEdeterministiF sn rel onditionsD it is unlikely tht the sme oservtion is otined for the sme stteF por exmpleD mesurement unertinty uses vrition in the oservtion of the sme seneF st 'ets the serh for the optiml poliy tht might e initilise with the sme eliefF his is reinfored y the smpling nd genertive proess tht involve rndomness @FfF etion RFRFIAF husD the three types of metris might vry even if the sme senrio is testedF IRS 5 Implementation, Tests and Analysis

  our system is one of the solutions proposed to void rewrd hking IHSD euse it fores the gent mke ompromisesF he remining of this setion presents eh group of us with exmples tht will e used to evlute our deisionEmking proessF IRU 5.3.2 Safety KPI he seleted us ims to identify ehviours tht re foridden y the highwy odeF sn every senrio relted with rod intersetion rossingD to stop within the re t the rossrod is foridden 3 F st inludes the rossing resD de(ned in etion QFPD where most of ollisions hppenF topping there loks the rossing of other vehile nd retes tr0 jmsF hen n intersetion exit is lokedD drivers re not supposed to ross the intersetionF his is slightly enfored in prne ompred with other ountries where heker ptterns tht signl the foridden re re usedD some exmples re shown in pigure SFR nd SFRF his unsfe re is shown in red in pigure SFRF Def :Unsafe Stop KPI sf the vehile position is loted within the unsfe re nd hs speed of 0 km/hD the deision result is onsidered s filedF sn the studied senriosD it is not mndtory for the to stop efore rossingF husD the metri ssoited with stop durtion is in the nvigtion tegoryF st would hve een di'erent if the stop mnoeuvre would hve een enfored y stop signF5.3.3 Trust KPIhriver trust is ruil to ept new systemD espeilly if vehile9s ontrols re shred QTD IRTF e driverGpssenger tht hs lost trust in the system is likely not to use it ginF et rossrodsD the most stressful moment is t the time of the rossingD the gp with other vehiles must e enough to gurntee tht no ollision will hppen nd tht there re no ys in the rossing reF ith PD gp my e redued to ouple of seonds IRU nd intersetion pity inresed QUF uh optimistion might pper fersome for pssengers tht prefer to oserve ertin time gp with other vehiles IPWF e riterion for the trust group is the time gp t the intersetion entrneF def :Gap KPI he time gp etween the nd the y mesured t the moment the enters the intersetionF sn the tested senriosD mintining the time gp over RsD s reommended in IPWD is mndtory for suessful exeutionD otherwise the test filedF he mesure hppens t the rossing event euse the time gp hnges during the pprohF e smll gp n e mesured t the eginning of the pprohing mnoeuvre ut redues euse of the deisionEmking tionsF (a) Unsafe area in France signalled with chequerboard marking (b) Unsafe area in Ireland signalled with a yellow pattern (c) View of the safe (green) and unsafe (red) areas to stop at a road intersection pigure SFR sntersetion lyout with unsfe nd sfe re to stop with exmples in two ountries IRW 5.3.4 Comfort KPI sn n utonomous vehileD pssenger tsks might e di'erentD s for exmple o0eEtype onesD thus ride omfort qulity eomes impertiveF gompred to trustD omfort mye ompromised in situtions tht require fst motion hngesD hene the seprtion in two groupsF ssenger9s omfort is not only orrelted with lterl nd longitudinl elertionsD ut lso with sudden hnge of elertionD lso known s jerk IPUD IPVF his is mesured s the rte of hnge of elertion with timeF his mkes jerk the (rst derivtive of elertionF sn our senrioD longitudinl elertion is ontrolled y the deisionEmking proessF gonsequentlyD hnges hppen to void ollision nd to dpt to the y ehviourF Def :Comfort KPI his is the mximum longitudinl jerk during the intersetion pprohF he jerk needs to remin etween ±3m/s 3 to gurntee pssenger omfort IPVF e striter rnge of ±2m/s 3 is hosen for this riterion to identify suessful runsF his lower ound llows to di'erentite system tht tkes preEemptive tions to void ollisions nd systems tht t only to void ollisionsF he former is less likely to get elow the ound ompred to the ltterF 5.3.5 Navigation KPI his us group determines the e0ieny of the vehile system to generte the motion tht relises the mnoeuvreF por the studied senriosD two uss re identi(edX trvel time nd the time stopped efore rossingF Def :Travel Time KPI st evlutesD how fst the system is le to pproh nd to ross the intersetionF ome senrios @FfF senrio eA requires the to stop s the y hs the priorityD thus ugmenting the mnoeuvre durtionF sn our senrioD given the initil ondition @see etion SFRA nd trvel time in simultion @see QFPFPFI pigure QFVAD the vehile should e le to ross in less thn 20s even if it must yieldF sn the senrio f nd gD the should ross (rstD thus lower ound of 15s is hosenF Def :Safe Stop KPI st onsiders time the vehile is t stop efore rossing the intersetionF sn ny of the three senriosD it is ompulsory to stopF roweverD the system n deide to ISH 5 Implementation, Tests and Analysis

5. 4

 4 Experiments and Resultshis setion presents the results otined with di'erent on(gurtions of the proposed deisionE mking systemD the evlution is mde pplying the uss desried in etion SFQF5.4.1 Parameters Exploration he proposed deisionEmking frmework hs mny prmetersD these re divided in two teE goriesX solver prmeters nd rewrd weightsF he rewrd weights re mde to ustomise how the system ehves during the pprohD wheres the solver prmeters 'et its pity to (nd the optiml solutionF he ltter were optimised to (nd solver prmeters tht re optiml for most of the weighting ftorsF here re two solver prmetersX the disount ftor γ nd the trdeEo' ontroller C @FfF RFRAF he lower these vlues reD the less the ywg hs to serh the poliy speF st lso inreses the numer of prtiles tht n e smpled t eh episodeF o optimise these prmetersD UHH runs were mdeF γ vlues were smpled5 etween H nd I with step HFHS nd C etween H nd IHH with step of IHF he prmeters for the rewrd weights were rndomly seletedF pigure SFS shows the perentge of suessful runs for eh omintion pigure SFS esult of the prmeter explortion for γ nd CD res with the lue ounds show high potentil for suessful rossing of prmetersF hese were found y using the ussF eres within the lue ounds disply high potentil of suessF he optiml on(gurtion ws not neessrily the one with the highest hne of suessD ut hs two hrteristisX neighours on(gurtion re lso suessful nd the prmeter vlues re not too igF he on(gurtion with γ = 0.85 nd C = 30 is hosen s it meets these hrteristisF he disount vlue results in serh horizon of IP seonds @see iqution RFPVAF his vlue is pproprite s it mens tht the serh horizon orresponds to most the mnoeuvre durtionF ith the solver prmeters setD rewrd weight vlues re exploredF hi'erent weight omintions re tested nd some on(gurtions with high potentil kept to e tested more intensivelyF wny di'erent on(gurtions hd su0ient performnes to e usedF por the remining of the studyD two on(gurtions were seleted to show nd to disuss the dvntge of the proilisti pproh nd the vlidtion methodF hey hve een seleted euse of high suess rteD nd ontrst ehviour in di'erent senriosD this is presented in following setionsF heir prmeters re shown in le SFQ with the shpe of the weighting funtion shown in pigure SFT with respet to the distne to the intersetionF he resemlnes nd di'erenes of these on(gurtion n e oservedF por othD none of the rewrd omponents is negtedD it shows tht ll the omponent of the rewrd funtion elements re importnt to ross the intersetionF he (rst on(gurtion ISQ le SFQ rmeters of eh weighting funtion of the rewrd model more the speed rewrd ompre to the seond tht vlues the ehviourl rewrds moreF he seletion proess of the on(gurtion prmeters my e improved y using reinforement lerning to utomtilly optimise the weight prmetersF roweverD their numers might not e prtil for suh pprohD they ould led to lol minimlF enother issue with reinforement lerning methods is the lrge numer of required runsF his ws not fesile with the urrent testing pltform s they need to run in prllel nd with elerted timeF 5.4.2 Behaviour Examples o show how the system ehves pprohing the intersetionD two exmples re disussed using the on(gurtion IF hese results re otined y pplying the test frmework presented in etion SFPF pigure SFU shows the two vehile speeds nd the tions ginst time ndintersetion distneF st demonstrtes how the system rets in the senriosF sn this senrio @eAD the y hs the priority keeping its speed while pprohing the intersetionF he is le to elerte to reh higher speed s the y is likely to ross (rstD thus risk is lowF trong deelertions @-1m/s 2 nd -2m/s 2 A re rrely hosen until the is lose to the ISR reward risk w r weight reward comfort w e weight reward expectation w i weight reward intention w s weight reward speed Conguration 2 weight functions pigure SFT ewrd weights s funtion of the distne to the intersetion for the two studied oneX ivolution of the speed in funtion of distne @A nd time @AF ghnges of elertion re shown y the olour of eh segment in senrio e with on(gurtion I intersetionF st orresponds with the redution of the omfort weight nd the importne given to speed lose to the intersetion entrneF he reson for deelertion fter 4s is let the y rosses with more ertinty tht expeted ehviour of the is to yieldF he redues its speed s it rrives to the intersetionD whih given the senrio is wht it will e s expetedF he sme on(gurtion is pplied for enrio f of our se study @the rosses nd the y stopsAD result is shown in pigure SFVF he keeps its speed to round T mGs until it is loser to the intersetion @pproximtely IH mAF et round Q m to prevent ollisionsD the y omes to stopF henD engges in the intersetion y elertingF es soon s the rossesD the y reEelertesF st n e oserved tht sudden hnge of elertions hppened t V seonds t the intersetion entrneD this is not omfortle for pssengersF IST 5 Implementation, Tests and Analysis (a) (b) pigure SFV ivolution of the speed in funtion of distne @oveA nd time @elowA ghnges of elertion re shown y the olour of eh segmentD enrio f with on(gurtion I ISU 5 Implementation, Tests and Analysis sn these exmplesD it n e oserved tht the is more prudent thn the model used in the simultionF he vehile moves t slower speedsF he simulted model gets the stte of the nd uses without onsidering unertintyD thus it n tke more riskF 5.4.3 Evaluation of the Decision-Making Performance. o understnd the system ehviourD it is neessry to uild sttistis over mny testsF por this purposeD the frmework presented in etion SFP is used to test the deisionEproessF he on(gurtions presented in etion SFRFI re usedF he environmentD y nd 9s dynmi re modeled using the ner simultorF he uss de(ned in etion @SFQA re used to evlute the performne on the deisionEmking lgorithm in the F o ompre the performne the developed systemD the model implemented in ner is usedF he is repled with this modelD tht is the sme used y the yF his model uses perfet knowledge of the environment stteF sts deisions re sed on the gp etween the two vehiles nd the signlistionF o generte sttistil dtD VHH senrios re reted to run the simultionsF his numer ws hosen euse it n over the senrio spe multiple timeF hese senrios vried the initil speedsD distnes to the intersetion nd di'erent signlistionsF le SFR shows the results of the simultion runsF erentges re provided for eh usF st inludes entries for the uss tht showed filuresF st is oserved tht multiple uss n e involved to sertin performne filureF he tle shows tht eh us n identify filures in the ehviourF ynly few filures re the produt of long stop durtionD ut the eptle limit for this us

  the intersection in less that a given time pigure SFW trvel time us studied with wg he pigure SFW shows the proility for this property to e true depending on the time it tkes to ross the intersetionF st n e oserved tht the system is unlikely to ross in less thn SsF st most likely tht the system rosses within IT seondsF st rehes plteu round fter PH seonds s the simultions stopsF es this proility does not reh ID the system is likely to fil to ross with proility of HFIF he unsfe stop nd sfe stop re reformulted eh into two fvv to nlyse the frequeny of stop nd their mximum durtionF por these metrisD smller vlue indite etter perE formneF gonsequentlyD it heks whether metri is ounded y bF he generl formul is G ≤ t m ≤ bF st sttes tht mD the metriD is lwys smller thn bF husD properties re

G

  (a) Probability to stop in less than a number of times less than a given amount of time Critical Zone Non critical zone (b) Probability to stop in less than given amount of time pigure SFIH fe nd unsfe stops nlysis with wg Saf e stop KP I ≤ t usstops ≤ b f requency G ≤ t utsstops ≤ b Duration esults otined y hnging the ounds re shown in pigure SFIHF st n e oserved thtthere is proility of HFWP tht the system doesn9t stop in the ritil re whih is desirleF he system is lso le to stop within the sfe re s required y some senrioD however y vrying the oundD it n e oserved tht the proility to stop more thn one time is not nullF y stop on multiple osion is not ommon nd should e investigtedF he durtion of stops shows tht there re often very smllD espeilly in the ritil reF st supports the hypothesis tht this is due to the vehile rwling into the intersetionF iven if it is unlikely some stops in the sfe re re often longD there is proility of HFIS tht some stops durtion re over T seondsF his nlysis tells more out the ehviour of the system nd ould e extended to other ussF he syntx ssoited lso llows to uild more omplex us nd to uild omintion of us tht ould e used to etter understnd the systemF ITQ 5.5 Conclusion sn this hpter frmework to test n eg is presented nd pplied to proilisti deisionE mking proessF st inludes the integrte of the within testing environment tht is simulted nd to determinte us to evlute the systemF hese uss re found y nlysing the senrio nd highwy odeF pour groups hve een identi(edD these reX fetyD trustD omfortD nd nvigtionF he nlysis nd results show tht the proposed deisionEmking proess n ross n intersetion while interting with nother vehileF he omprison of two on(gurtions shows di'erent performnes n e oserved in di'erent senriosF uss tht re not stis(ed y the system n e the next trget for the developersF es the senrio n e known in dvne with the mp nd tr0 signsD the two on(gurtions ould ohit nd e used only in the senrio tht is the most dptedF he testing frmework used for in this ghpter does not inludes ll the element of the generl rhitetureF rtiulrlyD the senrio explortionD tht is simpli(ed in our implementtionF o do more test with the urrent frmework would not dd more informtion on the ehviour of the system wsting time nd energyF his ould e improved with wg y llowing it to ontrol the senrio genertionF snsted of rndomly serh the senrio speD it looks for the limits etween suessful nd filed exeutionF st does this serh until it rehes numer of simultion su0ient to gurntee tht the limits hve een found with known degree of errorF he proposed rhiteture for testing n e used for ny kind of systemF st ould e used to test the other kind of deisionEmking proess disussed in ghpter PD espeilly for mhine lerning methods tht n e formlly vlidtedD due to unidenti(le prmetersF ITR 6 Conclusion 6.1 Summary of Findings eutonomous vehiles hve een in development for mny yersF hey re expeted to redue rod ftlities nd to hnge trnsporttionF roweverD their deployment is stllingF st is unlikely tht full utonomy everywhere is hievle without tehnologil rekthrough in mny dominsF xeverthelessD utonomy in prede(ned onstrined opertionl domin n e possileF sn urn resD omplexity omes from intertion with other entities shring the immedite environment of the sujet vehileF o pereive nd predit the motion of these entities is sujet to muh unertinties thus deisionEmking proess must onsider these to pln the vehile motionF his reserh ddressed the nvigtion of n utonomous vehile in one of the most omplex senrios of the rod networkD tht is the rossing of rod intersetionsF por the ujet vehile to deide wht tion to tke nextD the mnoeuvre of other drivers must e understoodF his is hieved y nlysing the vehile motion nd its ontextF he pproh tken is sed on lerningEsed pprohD nd method to prtition the spe into ontextully relevnt resF esults showed tht it ws possile to infer driver mnoeuvre nd tht mixing syntheti nd rel trjetories helped to simplify the lerning proessF o ontextulize the vehile motionD disretistion of motion ptterns is relisedF he deision proess uses proilisti frmework

  oded s prt of the e softwre stkF he performne ws evluted pplying series of purposely de(ned key performne inditorsF ITS he ontext ws determined y nlysing motion ptterns lernt using qussin proessesF hese re trined with trjetories of vehiles rossing n intersetion through simultion modelsF he resulting motion ptterns re segmented to determine rossing nd merging res y (nding overlps etween themF e single motion pttern is disretized in multiple zonesD these divide the di'erent phses of driver motion rriving t n intersetionF fy omining them with the mnoeuvre understndingD the mnoeuvre lssi(tion is improvedF o lssify driver mnoeuvresD ndom porest glssi(ers re pplied to lern motion fetures ssoited with the three possile mnoeuvres t n intersetionF hese reX grossD ield nd topF e dtset mde of syntheti nd rel trjetories is used to redue the need for lrge nturlisti dtsetF esults otined y lssifying the mnoeuvre of rel driver showed tht the proposed frmework is etter thn other possile implementtionsF e lssi(er using only syntheti dt n determine the mnoeuvreD ut with few rel dt dded to the dtsetD the performne inreses rpidlyF his redues the need for lrge nturlisti dtsetD tht re omplex nd ostly to otinF e rtilly yservle wrkov heision roess uses the mnoeuvre lssi(tion s n oserE vtion of other driver intended mnoeuvreF st tkes into ount unertinties of the intertion etween the sujet vehile nd the other driverF he sujet vehile is rewrded to intertD to mintin omfortD low riskD nd to mintin n pproprite speedF hese re lned di'erently while the sujet vehile rrives t the intersetionF he resulting model is solved y using n online ywh solverD nmely ywgF st evlutes poliies y smpling the urrent elief nd y simulting the e'et of the poliy tionsF e generi testing frmework is developed to interpret the deisionEmking proess performneF st uses the rhiteture proposed in the inleEsQ iuropen projet on eri(tion nd liE dtionF uey erformne snditors re formulted to nlyse the ehviour of system under tests with respet to the rod intersetion rossing senrioF pour tegories of us re foundX omfortD sfetyD trust nd nvigtionF st determines di'erent prolems of the tested systemF he pproh showed how the deisionEmking frmework in two di'erent on(gurtions ehve while pprohing nd rossing n intersetionF xonethelessD given the strit onditionD PH7 of exeutions do not stisfy t lest one usF his nlysis is extended y pplying ttistil wodel gheking to vry the ound of uss to improve the system performne interprettionF ITT 6.2 Conclusions he results otined in this thesis led to three mjors onlusions whih re desried elowX IF Interaction between vehicles is achieved by using their intended manoeuvre. o ross sfely n intersetionD the proposed deisionEmking proess resons with the intended mnoeuvre of the other vehileF fy ompring it with the expeted mnoeuvreD the system n determine how to intert with the other vehileF esults from our simultion runsD showed tht the system plnned its motion s it rrives to the rossrod nd ntiipted tions of the other vehileF o otin the intended mnoeuvreD the motion nd ontext of nother vehile re used to lssify its mnoeuvreF houghD the resultnt output inludes some degree of unertintyF he result of the deisionEmking proess is n tion in whih intertion nd risk re lnedF PF Uncertainties from perception and interaction should be included as part of the decision processF he formlism of rtilly yservle wrkov heision roess inludes elements to onsider pereption unertinties @the oservtion modelA nd how the sitution hnges @trnsition funtionAF hese re used to evlute tions y onsidering its likely onsequenesF fy onsidering unertintiesD trdeEo' is mde etween unlikely hzrdous events nd likely lowErisk evolution of the situtionF he overll omplexity nd its pplition for relEtime system led to hoose n online solverF his solves the prolem prtilly for time horizonF roweverD it results in vritions of the sujet vehile ehviour given the sme situtionF iven ifD the vehile drives s if under humn ontrolD it is di0ult to interpret the result of suh system QF Key Performances indicators facilitate a better interpretation of the system performance. he omplexity of system to e deployed for utonomous vehile mkes stndrd methods for vlidtion omplexF imultion nd intensive testing re key elements of the vlidtion proessesF he proposed uey erformne snditors llow to identify tests tht re not onform with the expeted ehviour t the rod intersetionF pour tegories of uss re proposed to study di'erent driving spetsF hese re omfortD sfetyD trust nd nvigtionF gomined with methods suh s ttistil wodel ghekingD these n e used to determinte how omplex nd nonEdeterministi systems ehveF ITU 6.3 Perspectives he results of this reserh work led to industril interest s well s the need for further work in this reF he following setion presents some generl perspetives nd disusses them in the ontext of the three previous onlusions mentioned oveF hespite demonstrtion of the pproh vlidity using simultion modelD this should e vlidted y testing on physil prototypesF he frmework shll e integrted into the rhiteture of enult9s prototypesF roweverD due to the tightly integrtion of the deisionEmking nd nvigtion systems due to opertionl onstrintsD integrtion proved to e omplexF heir system is not )exile in order to ensure sfety nd oherene etween systemsF gonsequentlyD modi(tions re required in the nvigtion system to integrte the proposed deision system nd interfes need to e developed for the supervisory nd ontrol systemsF sf performnes re on(rmed with these experimenttionsD further simultions n e done to re(ne the resultsF o deploy the solutions on rel rodsD the slility of the pproh needs to e demonstrtedF sn generlD the slility is relted to the e'ort put into the system to ddress lrger prolemF wost likelyD it requires more dtD more omputtionl power nd more roust implementtionF he spei( slility limits of eh ontriution re disussed elowF sn n industril ontextD for vehile generlist mnufturerD where ost is mjor onernF he results ould e pplied to n edvne hriver essistne ystem @eheAD with ei vevel P of utonomyD in ddition to long term pplitions of vevel R vehilesF IIF he ojetive is to help humns during the driving tsk @eFgF lne keepingD emergeny rke systemAF he di'erentition etween intention nd expettion hs lredy een used to wrn driver out risks IPP or to predit the type of ssistne required ISP t rossrod intersetionsF he proposed deisionE mking system n e used to provide two types of ssistne while pprohing the rossrodF pirstD n tive ssistne ould inrese or derese the elertion of the vehile if driver9s tions re too di'erent ompred to the system deisionsF his type of ssistne nudges the driver to tke orret tions nd redues risksF his system ould lern from drivers nd dpt its rewrd model in funtion of the driverF he seond is pssiveD it ompres the vlue of driver9s tion with the vlue of the est tionF his omprison ould e used to sore the driver9s ehviour fter rossing the intersetionF his evlutes how well drivers interted with other t rossrodF his sore n e displyed on n rws to wrn driver out their dngerous ehvioursF hese types of ssistne n e tested with the rhiteture of the inleEsQ projetD tht inludes the opertor @in our se driverA in the testing frmeworkF st is to note ITV 6 Conclusion tht the proposed solution n e well ligned with the future sfety vehile evlution systems under the iuronp frmeworkF 6.3.1 Behaviour Understanding o sle the lerning pproh proposed in ghpter QD reording of trjetories t di'erent types of intersetions re requiredF st would show how the pproh generlize to new intersetionsF vikelyD di'erent models will e required nd dpt to spei( intersetionF roweverD synthetimnoeuvre hs een showed to improve the lssi(tionF husD the time required to oserve n intersetion n e reduedF ehiles re not the only entities interting with the sujet vehileD there re lso pedestrins or ylistsF imilrlyD these hve n intention motivted y their golD hnging the mnner they intert with vehileF et rossrodD the presene of pedestrin rossing is likelyD resulting in frequent intertionsF here pedestrins n hve di'erent rossing intentions tht 'et the mnner they ross ISQD ISRF 6.3.2 Probabilistic Decision-making he min limittion to sle the deisionEmking proess is the numer of vehiles present in the seneF et the timeD single other vehile is interting with the sujet vehileF o inrese the numer of vehiles is required to ddress omplex situtionF he (rst onsequene is growth of the stteEspe nd oservtionEspeF he resulting model will e hrder to solveD s the elief distriution will e hrder to estimteF st ould e ompensted with etter solver or more omputtionl power VUF e more dvned strtegy onsists of smpling vehile sttes di'erently sed on importne riterionF his ould e for the exmple their distne to the intersetionF gonsequentlyD vehile fr wy from the intersetion will hve fewer prtile desriing its sttes thn vehile lose to the intersetionF he seond onsequene is the omplexity of intertion etween vehilesF fy inresing the numer of vehilesD the ehviour vriles will e dependent of eh of the other vehileF o predit in this ondition is di0ult euse there is n interdependene etween eh vehileF enother hllenge for deisionEmking proess is resoning with re of the environment tht re not yet oservedF his is highly di0ult s there re mny of them in urn re @eFgF re etween prk vehilesD in front of us or oluded intersetion rnhesA from whih vulnerle ITW entities might pperF roilisti pprohesD similr to the one proposed in ghpter RD re unlikely to sle orretly s they will require lot of omputtion power IQSD ISSF he prolem is used y the worst event eing the pprition in front of the vehile of fstEmoving pedestrinD tht needs to e highly penlised in the rewrd modelD nd tht none of the longitudinl tion helps to redue this risk without resulting of slowEmoving vehileF e potentil solution is to inorporte the deision of the lterl motion of the vehileF fy ringing the vehile to the left side of its lneD hzrd from the oluded res re redued nd sujet vehile speeds mintinF oughD this inreses the tionEspe nd redues the hne to (nd the optiml poliy evlutionF he proposed model hs mny prmeters tht need to e tunedF his optimistion proess ould e improved y using geneti lgorithm IST or reinforement lerning WWF hese methods use tril nd errors to selfEimprove in simultionD ut there is risk tht the solution over(t on the simultion environmentF ome new reserhes on trnsfer lerning shows howD for simple environmentD lerned models n e trnsferred into di'erent spe ISUF his hs lso some impt on the vlidtion tht lso uses simultionF sf the trining nd vlidting environment re too similrD euse they re using the sme simultorD the evlution of the performnes might not trnsfer to the rel worldF heep lerning n e prt of the solution to model elements tht re di0ult to (nd nlytillyF roweverD this omes to loss of explinility in system tht is lredy omplexF his omplexity inrese must go jointly with the development of testing solutionF 6.3.3 Validation o vlidte the system nd to uild the sttistil rgumentD there is need to extend the E pilities of the testing pltformF he numer of simultions need to e inresedF st n e improved y running multiple exeutions in prllel nd y elerting the time in the simE ultionF xote tht the ltter is not omptile with the proposed D s it uses the period etween two oservtions to evlute the vlue funtionF iven if the numer of tests is inresedD to ontinue to rndomly explore the senrio spe would e wste of time nd energyF wore omplex senrios hve more vrilesD thus the senrio explortion omplexity inreses drsE tilly the numer of senrios to testsF purtherD simultors re often ostly to develop nd remins essile to only few peopleF sn the reent yers openEsoure simultors hve een developed ut these re more foused on mhine lerning pplition nd not vlidtion ISVF gonsequentlyD the senrio explortionD prt of the eri(tion nd lidtion mngement in IUH the inleEsQ rhitetureD eomes n extremely vlule toolF st (nds vlule test senrios prior running the simultionF he omplete ttistil wodel gheking frmework hs retroE tive loop tht n ontrol the senrio explortionF sts ojetive is to hoose whih senrio on(gurtion is the most vlule to inrese the ury of the us proilisti evlutionF he vlidtion prolem remins ore hllenge for the deployment of omplex utonomous systemF his is not only n e'ort prolem from the industry to test system ut lso reserh prolem to (nd the orret methodsF hese re likely to e di'erent given the nture of the tested systemF IUI I F grmtonD F F qeddesD eF ykenfelsD et al.D sing tehnology to eliminte tr0 ongestionD Journal of Institutional and Theoretical Economics (JITE)D volF IUSD noF ID ppF IPT!IQWD PHIWF IQ P F ossD olvo sys goodye to gsolineF httpXGGwwwFsoopFdeveloppementE durleFgouvFfrGenGgenerlEpresenttionEWFhtmlD PHITF lst essed HPGPHIVF IQ Q orld relth yrgniztionD od tr0 injuriesF http://www.who.int/mediacentre/ factsheets/fs358/en/D tnF PHIVF lst essed HPGPHIVF IQD IR R xF rüswetter nd uF fenglerD hy should i use dsc dvned driver ssistne systems nd the elderlyX knowledgeD experiene nd usge rriersD in Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : driving assessment 2013D niversity of sowD PHIQF IQ S iF hF hikmnnsD heveloping the sense of vision for utonomous rod vehiles t uniwmD ComputerD volF SHD ppF PR!QID heemer PHIUF IRD TH T F hrunD wF wontemerloD rF hhlkmpD hF tvensD eF eronD tF hieelD F pongD tF qleD wF rlpennyD qF ro'mnnD et al.D tnleyX he root tht won the drp grnd hlE lengeD Journal of eld RoboticsD volF PQD noF WD ppF TTI!TWPD PHHTF IR U gF rmsonD tF enhltD hF fgnellD gF fkerD F fittnerD wF xF glrkD tF holnD hF hugginsD F qltliD gF qeyerD et al.D eutonomous driving in urn environmentsX foss nd the urn hllengeD Journal of Field RoboticsD volF PSD noF VD ppF RPS!RTTD PHHVF IRD ISD QP V wF rrrisD row google9s utonomous r pssed the (rst uFsF stte selfEdriving testF httpsXGGspetrumFieeeForgGtrnsporttionGdvnedErsGhowEgooglesEutonomousE rEpssedEtheE(rstEusEstteEselfdrivingEtestD eptF PHIRF lst essed IPGPHIVF IR IUP BIBLIOGRAPHY W qoogleD elfEdriving r testX teve mhnF https://www.youtube.com/ watch?v=cdgQpa1pUUED mrh PHIPF lst essed IPGPHIVF IS IH eutersD enult ims to lunh pris rEshring sheme in septemerF httpsXGGwwwFreutersFomGrtileGusEfrneEutosGrenultEimsEtoElunhEprisErE shringEshemeEinEseptemerEidufxItIHtD PHIVF lst essed on PPGHUGIVF IS II eiD xonomy nd de(nitions for terms relted to driving utomtion systems for onE rod motor vehilesF httpsXGGwwwFseForgGstndrdsGontentGjQHITPHIVHTGD tune PHIVF essed on PPGHSGIVF IT IQ ymoD ymo9s fully selfEdriving vehiles re hereF httpsXGGmediumFomGwymoGwithE wymoEinEtheEdriversEsetEfullyEselfEdrivingEvehilesEnEtrnsformEtheEwyEweEgetE roundEUSeWTPPeVPWD PHIUF lst essed on PPGHSGIVF ITD QQ IR ymoD ymo9s sfety reportX how we re uilding sfer driverF httpsXGGmediumFomGwymoGwymosEsfetyEreportEhowEweEreEuildingEEsferEdriverE eSfIHdRPSD PHIUF lst essed on PPGHSGIVF IT IS F eF hzinoD wF rrisD nd fF verdD ere onsumers willing to py to let rs drive for themc nlyzing response to utonomous vehilesD Transportation Research Part C: Emerging TechnologiesD volF UVD ppF ISH ! ITRD PHIUF IT IT inleEQD lidtion nd testing of omplex utomted systemsF https://www. enable-s3.eu/F lst essed HWGPHIWF ITD IQU IU iF qonzlezD ypen utonomous sfetyF httpsXGGgithuFomGvoygeGopenEutonomousE sfetyD PHIVF lst essed on PPGHSGIVF IU IV iuropen gommissionD r0 sfety si fts PHIUX tuntionD PHIUF lst essed HSGHPGPHIVF IWD PHD PID TV IW iuropen gommissionD eout reF https://ec.europa.eu/transport/road_safety /specialist/statistics_enD PHIUF lst essed HSGHPGPHIVF PH IUQ BIBLIOGRAPHY PH shington tte heprtement of trnsporttionD oundout ene(tsF httpsXGGwwwFwsdotFwFgovGfetyGroundoutsGene(tsFhtmD PHIVF lst essed on PPGHSGIVF PID TV PI vF eF odegerdtsD fF xeversD fF oinsonD tF ingertD F uooneD tF fnsenD F xguyenD tF wqillD hF tewrtD tF uggettD et al.D ignlized intersetionsX informtionl guideD tehF repFD pederl righwy edministrtion eserh nd ehnologyD PHHRF PID TW PP vF pletherD F ellerD iF ylsonD hF wooreD F uuwtD tF rowD tF tF veonrdD sF willerD wF gmpellD hF ruttenloherD et al.D he ws!gornell ollision nd why it hppenedD Journal of Field RoboticsD volF PSD pF UUS!VHUD PHHVF PPD QPD SR PQ ivn ekermnD ptl tesl selfEdriving r rsh reminds us tht roots ren9t perE fetF httpsXGGspetrumFieeeForgGrsEthtEthinkGtrnsporttionGselfEdrivingGftlEteslE utopilotErshEremindsEusEthtErootsErentEperfetD PHITF lst essed HSGHTGPHIVF PP PR pF wF pvroD xF xderD F yF iurihD wF rippD nd xF rdrjuD ixmining ident reports involving utonomous vehiles in liforniD PLOS ONED volF IPD ppF I!PHD HW PHIUF PP PS F wF vlle nd F hrmD yn motion plnning in hngingD prtilly preditle environmentsD The International Journal of Robotics ResearchD volF ITD noF TD ppF UUS! VHSD IWWUF PR PT wF frierD gF vugierD yF imoninD nd tFsñezEquzmánD puntionl disretiztion of spe using gussin proesses for rod intersetion rossingD in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)D ppF IST!ITPD xov PHITF PV PU wF frierD gF vugierD yF imoninD nd tF sñezEquzmánD glssi(tion of drivers mnoeuvre for rod intersetion rossing with synthethi nd rel dtD in 2017 IEEE Intelligent Vehicles Symposium (IV)D ppF PPR!PQHD tune PHIUF PVD RT PV F lrih nd wF wurerD roilisti online pomdp deision mking for lne hnges in fully utomted drivingD in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)D ppF PHTQ!PHTUD yt PHIQF QP IUR BIBLIOGRAPHY PW rF fiD F giD xF eD hF rsuD nd F F veeD sntentionEwre online pomdp plnning for utonomous driving in rowdD in 2015 IEEE International Conference on Robotics and Automation (ICRA)D ppF RSR!RTHD wy PHISF QP QH wF foutonD eF gosgunD nd wF tF uohenderferD felief stte plnning for utonomously nvigting urn intersetionsD in 2017 IEEE Intelligent Vehicles Symposium (IV)D ppF VPS!VQHD tune PHIUF QPD IIPD IISD IPQ QI F qindeleD F frehtelD nd F hillmnnD verning driver ehvior models from tr0 oE servtions for deision mking nd plnningD Intelligent Transportation Systems Magazine, IEEED volF UD ppF TW!UWD pring PHISF QP QP wF fojrskiD hF hel estD hF hworkowskiD fF pirnerD fF pleppD F qoylD vF hF tkelD wF wonfortD F wullerD tF hngD et al.D ind to end lerning for selfEdriving rsD arXiv preprint arXiv:1604.07316D PHITF QPD THD TI QQ tF snezEquzmánD gF vugierD tFEhF oderD nd F hrunD Autonomous Driving: Context and State-of-the-ArtD ppF IPUI!IQIHF vondonX pringer vondonD PHIPF QQD QVD QW QR vF pridmnD rumnEentered utonomous vehile systemsX riniples of e'etive shred utonomyD arXiv preprint arXiv:1810.01835D PHIVF QRD QS QS iF ghD eF hF hrgnD nd F F rinivsD ereived root pilityD 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)D ppF SRI!SRVD PHISF QS QT F rF rungD uF fhtiD F eeelD nd eF hF hrgnD istlishing pproprite trust vi ritil sttesD 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)D yt PHIVF QSD IRV QU uF hresner nd F toneD wultigent tr0 mngementX e reservtionEsed intersetion ontrol mehnismD in The Third International Joint Conference on Autonomous Agents and Multiagent SystemsD ppF SQH!SQUD tuly PHHRF QTD IRV QV wF ligD yF fu'etD nd yF imoninD topEfree strtegies for tr0 networksX heentrlized onEline optimiztionD in Proceedings of the Twenty-rst European Conference on Articial IntelligenceD iges9IRD ppF IIWI!IIWTD sy ressD PHIRF QT QW uF utsros nd wF hintiD A Conceptual 5G Vehicular Networking ArchitectureD ppF SWS! TPQF ghmX pringer snterntionl ulishingD PHIUF vehiles nd rod infrstruture for rod sfetyF httpXGGwwwFsfespotEeuForgGD PHIHF lst essed HSGHPGPHIVF QT RP vF ghen nd gF inglundD goopertive intersetion mngementX e surveyD IEEE Transactions on Intelligent Transportation SystemsD volF IUD ppF SUH!SVTD pe PHITF QT RQ tF sñezEquzmánD F vefevreD eF wokkdemD nd F odhimD ehile to vehile ommuE nitions pplied to rod intersetion sfetyD (eld resultsD in 2010 13thInternational IEEE Conference on Intelligent Transportation Systems (ITSC),D ppF IWP!IWUD ept PHIHF QU RR tF F eF elexnder wF weystelD Intelligent Systems: Architecture, Design, and ControlF tohn iley 8 onsD snFD PHHIF QVD QWD SV RS wF imonD F wilzD uF emendeD nd rFEwF qrossD gomplexEyoloX en eulerEregionEproposl for relEtime Qd ojet detetion on point loudsD in European Conference on Computer VisionD ppF IWU!PHWD pringerD PHIVF RID RU RT vF ummelhrdD eF xègreD wF errollzD nd gF vugierD roilisti qridEsed golliE sion isk redition for hriving epplitionD in ISERD tune PHIRF RI RU tF hng nd F inghD vomX vidr odometry nd mpping in relEtimeFD in Robotics: Science and SystemsD volF PD pF WD PHIRF RP RV pF viD F fonnifitD tF snezEquzmnD nd gF inouneD vneElevel mpEmthing with integrity on highEde(nition mpsD in 2017 IEEE Intelligent Vehicles Symposium (IV)D ppF IIUT!IIVID tune PHIUF RP RW wF xolteD F irnstD tF ihelmnnD nd wF wurerD epresenting the unkown E impt of unertinty on the intertion etween deision mking nd trjetory genertionD CoRRD volF sGIVHRFHVVUID PHIVF RQ SH F vefevreD hF squezD nd gF vugierD e survey on motion predition nd risk ssessment for intelligent vehilesD ROBOMECH JournalD volF ID noF ID ppF I!IRD PHIRF RR SI tF smussenD Information Processing and Human-Machine Interaction: An Approach to Cognitive EngineeringF xew orkD xD eX ilsevier iene snFD IWVTF RR IUT BIBLIOGRAPHY SP xF eF tnton nd F wF lmonD rumn error txonomies pplied to drivingX e generi driver error txonomy nd its implitions for intelligent trnsport systemsD Safety Sci-enceD volF RUD noF PD ppF PPU ! PQUD PHHWF RR SQ F vefevreD gF vugierD nd tF sñezEquzmánD isk ssessment t rod intersetionsX gompring intention nd expettionD in 2012 IEEE Intelligent Vehicles Symposium (IV)D ppF ITS!IUID tune PHIPF RTD RUD SID SPD TTD IIVD IIWD IPT SR eF ermndD hF pillitD nd tF sñezEquzmánD wodelling stop intersetion pprohes using gussin proessesD in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)D yt PHIQF RTD RUD UID UPD UQD UU SS F viuD FEF uimD nd wF rF engD roilisti rod ontext inferene for utonomous vehilesD in 2015 IEEE International Conference on Robotics and Automation (ICRA)D ppF ITRH!ITRUD wy PHISF RTD RU ST F ivrmn nd wF wF rivediD vooking t vehiles on the rodX e survey of visionE sed vehile detetionD trkingD nd ehvior nlysisD IEEE Transactions on Intelligent Transportation SystemsD volF IRD ppF IUUQ!IUWSD he PHIQF RUD RV SU F hrunD F furgrdD nd hF poxD Probabilistic roboticsF ws pressD PHHSF RU SV pF qrossD tF tordnD pF eningerD pF ulnnerD nd fF hullerD oute nd stopping intent predition t intersetions from r )eet dtD IEEE Transactions on Intelligent VehiclesD volF ID ppF IUU!IVTD tune PHITF RWD SHD SQD WVD WW SW wF qF yrtizD tF pritshD pF uummertD nd eF qepperthD fehvior predition t multiple timeEsles in innerEity senriosD in 2011 IEEE Intelligent Vehicles Symposium (IV)D ppF IHTV!IHUQD tune PHIIF RWD SH TH hF tF hillipsD F eF heelerD nd wF tF uohenderferD qenerlizle intention predition of humn drivers t intersetionsD in 2017 IEEE Intelligent Vehicles Symposium (IV)D ppF ITTS!ITUHD tune PHIUF RW TI eF wriD F wisuD nd uF pujimurD rediting unexpeted mneuver while pprohing intersetionD in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)D ppF PPPS!PPPWD xov PHITF RWD SH IUU TP F fF emsluD eF romifrD pF efghhD F myrD nd eF uurtD hriver ehvior modeling ner intersetions using support vetor mhines sed on sttistil feture extrtionD in 2015 IEEE Intelligent Vehicles Symposium (IV)D ppF IPUH!IPUSD tune PHISF RW TQ eF uhosroshhiD iF yhnEfrD nd wF wF rivediD urround vehiles trjetory nlysis with reurrent neurl networksD in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)D ppF PPTU!PPUPD xov PHITF RW TR qF eoudeD F hesrjuD vF tephensD nd tF rowD hriver ehvior lssi(tion t interE setions nd vlidtion on lrge nturlisti dt setD IEEE Transactions on Intelligent Transportation SystemsD volF IQD ppF UPR!UQTD tune PHIPF SHD SID SQD VTD WW TS F treuel nd uF rF ro'mnnD redition of driver intended pth t intersetionsD in 2014 IEEE Intelligent Vehicles Symposium ProceedingsD ppF IQR!IQWD tune PHIRF SHD SID SQ TT tF hulzD gF rumnnD tF vöhnerD nd hF furshkD sntertionEwre proilisti ehvior predition in urn environmentsD in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)D ppF QWWW!RHHTD siiiD PHIVF SHD SID SP TU F vefevreD gF vugierD nd tF sñezEquzmánD ixploiting mp informtion for driver intention estimtion t rod intersetionsD in 2011 IEEE Intelligent Vehicles Symposium (IV)D ppF SVQ!SVVD tune PHIIF SI TV F vefevreD tF sñezEquzmánD nd gF vugierD gontextEsed estimtion of driver intent t rod intersetionsD in 2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) ProceedingsD ppF TU!UPD epril PHIIF SID SQ TW F frehtelD F qindeleD nd F hillmnnD olving ontinuous pomdpsX lue itertion with inrementl lerning of n e0ient spe representtionD in In Proc. Int. Conf. on Machine LearningD PHIQF SI UH wF F hirzi nd fF F worrisD vooking t intersetionsX e survey of intersetion monE itoringD ehvior nd sfety nlysis of reent studiesD IEEE Transactions on Intelligent Transportation SystemsD volF IVD ppF R!PRD tn PHIUF SQ UI F onziniD urn signl usge rte resultsX e omprehensive (eld study of IPDHHH oserved turning vehilesD tehF repFD ei ehnil perD PHIPF SQ IUV BIBLIOGRAPHY UP gF fker nd tF holnD r0 intertion in the urn hllengeX utting oss on its est ehviorD in International Conference on Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJD ppF IUSP!IUSVD ept PHHVF SRD SS UQ tF uF osenlttD hmnX e distriuted rhiteture for moile nvigtionD Journal of Experimental & Theoretical Articial IntelligenceD volF WD noF PEQD ppF QQW!QTHD IWWUF SR UR eF fhD gF fumnD F pruqueD wF plemingD gF erwelpD gF einholtzD hF rongD eF iksD F eleriD hF endersonD et al.D ydinX em vitortngo9s entry in the drp urn hllengeD Journal of Field RoboticsD volF PSD noF VD ppF RTU!RWPD PHHVF SRD SS US hrpD rn ghllenge oute xetwork he(nition pile @xhpA nd wission ht pile @whpA pormtsD PHHUF SS UT F viu nd F yzgunerD rumn driver model nd driver deision mking for intersetion drivingD in Intelligent Vehicles Symposium, 2007 IEEED ppF TRP!TRUD tune PHHUF STD VP UU eF purd nd vF liD inling sfe utonomous driving in relEworld ity tr0 using multiple riteri deision mkingD Intelligent Transportation Systems Magazine, IEEED volF QD ppF R!IUD pring PHIIF ST UV F egeleD sing ontologyEsed tr0 models for more e0ient deision mking of uE tonomous vehilesD in Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08)D ppF WR!WWD wrh PHHVF ST UW vF hoD F shiseD F skiD F viuD nd F oshikwD pst deision mking usE ing ontologyEsed knowledge seD in 2016 IEEE Intelligent Vehicles Symposium (IV)D ppF IUQ!IUVD tune PHITF ST VH F fellmnD e mrkovin deision proessD Indiana Univ. Math. J.D volF TD ppF TUW!TVRD IWSUF SUD IPW VI wF vF utermnD Markov Decision Processes: Discrete Stochastic Dynamic ProgrammingF xew orkD xD eX tohn iley 8 onsD snFD Ist edFD IWWRF SU VP eF F gssndrD e survey of pomdp pplitionsD in Working notes of AAAI 1998 fall symposium on planning with partially observable Markov decision processesD volF IUPRD IWWVF SU IUW VQ vF F uellingD wF vF vittmnD nd eF F gssndrD lnning nd ting in prtilly oservle stohsti dominsD Articial intelligenceD volF IHID noF ID ppF WW!IQRD IWWVF SU VR F mith nd F immonsD reuristi serh vlue itertion for pomdpsD in Proceedings of the 20th conference on Uncertainty in articial intelligenceD ppF SPH!SPUD ees ressD PHHRF SW VS F hrunD wonte rlo ywhsD in Advances in Neural Information Processing Systems 12D ppF IHTR!IHUHD ws ressD PHHHF SWD IPW VT xF oyD qF qordonD nd F hrunD pinding pproximte ywh solutions through elief ompressionD Journal of Articial Intelligence ResearchD PHHRF SW VU rF uurniwtiD hF rsuD nd F F veeD rsopX i0ient pointEsed pomdp plnning y pproximting optimlly rehle elief spesD in In Proc. Robotics: Science and SystemsD PHHVF SWD ITW VV hF ilver nd tF enessD wonteErlo plnning in lrge pomdpsD in Advances in Neural Information Processing Systems 23D ppF PITR!PIUPD gurrn essoitesD snFD PHIHF SWD IPWD IQH VW F qelly nd hF ilverD gomining online nd o1ine knowledge in utD in Proceedings of the 24th International Conference on Machine LearningD sgwv 9HUD ppF PUQ!PVHD egwD PHHUF SW WH hF ilverD eF rungD gF tF wddisonD eF quezD vF ifreD qF n hen hriessheD tF hritE twieserD sF entonoglouD F nneershelvmD wF vntotD et al.D wstering the gme of go with deep neurl networks nd tree serhD NatureD volF SPWD noF USVUD ppF RVR!RVWD PHITF SW WI vF eiD Planning under Uncertainties for Autonomous Driving on Urban RoadF hh thesisDxtionl university of ingporeD PHITF SWD IIS WP F frehtelD F qindeleD nd F hillmnnD roilisti deisionEmking under unertinty for utonomous driving using ontinuous pomdpsD in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)D ppF QWP!QWWD yt PHIRF SWD THD TSD IIPD IPQ WQ gF rumnnD wF fekerD hF eltho'D hF venzD nd gF tillerD heision mking for uE tonomous driving onsidering intertion nd unertin predition of surrounding vehilesD in Intelligent Vehicles Symposium (IV), 2017 IEEED ppF ITUI!ITUVD siiiD PHIUF TH IVH WR F viuD F F uimD F endletonD nd wF rF engD itutionEwre deision mking for utonomous driving on urn rod using online pomdpD in 2015 IEEE Intelligent Vehicles Symposium (IV)D ppF IIPT!IIQQD tune PHISF TH WS uF rF ryD F tF itwikiD nd F ilersteinD ynline deisionEmking for slle uE tonomous systemsD in Proceedings of the Twenty-Sixth International Joint Conference on Articial Intelligence, IJCAI-17D ppF RUTV!RUURD PHIUF TH WT wF rsenjger nd rF ersingD ersonliztion in dvned driver ssistne systems nd utonomous vehilesX e reviewD in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)D ppF I!UD yt PHIUF TI WU wF uudererD F qultiD nd F furgrdD verning driving styles for utonomous vehiles from demonstrtionD in 2015 IEEE International Conference on Robotics and Automation (ICRA)D ppF PTRI!PTRTD wy PHISF TI WV hF ierr qonzálezD ÖF irkentD F omeroEgnoD tF hingoyeD nd gF vugierD wodeling hriver fehvior prom hemonstrtions in hynmi invironments sing ptiotemporl vttiesD in ICRA 2018 -Proceedings of the 2018 IEEE International Conference on Robotics and AutomationD @frisneD eustrliAD wy PHIVF TI WW vF pridmnD fF tenikD nd tF erwilligerD heepr0X hriving pst through hense r0 with heep einforement verningD ArXiv e-printsD tnF PHIVF TPD IUH IHH hF sseleD F himiD eF gosgunD uF urmninD nd uF pujimurD xvigting yluded sntersetions with eutonomous ehiles using heep einforement verningD ArXiv e-printsD wy PHIUF TP IHI eF fewleyD tF igleyD F viuD tF rwkeD F henD FEhF vmD nd eF uendllD verning to hrive from imultion without el orld velsD arXiv e-printsD pF rivXIVIPFHQVPQD heF PHIVF TPD TQD VU IHP eF uendllD tF rwkeD hF tnzD F wzurD hF edD tFEwF ellenD FEhF vmD eF fewleyD nd eF hhD verning to drive in dyD 2019 International Conference on Robotics and Automation (ICRA)D wy PHIWF TPD TSD VS IHQ esD fety (rst for utomted drivingF httpsXGGwwwFptivFomGdosGdefultE soureGwhiteEppersGsfetyE(rstEforEutomtedEdrivingEptivEwhiteEpperFpdfD PHIWF vst essed on HVGHVGIWF TRD IQU IVI

  Autonomous Vehicles: Social and Financial Rational . . . . . . . . . 13 1.2 Road Intersections Crossing . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2.1 Crossing at Road Intersection Compared to Other Scenario . . . . . . . 17

	1 Introduction
	1.1
	Bibliography	172
	II

1.2.2 Accident Statistics at Road Intersections . . . . . . . . . . . . . . . . . . 19 1.2.3 Decision-Making for Crossing at Road Intersection . . . . . . . . . . . . 22 1.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.3.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.3.2 Thesis Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.3.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.4 Thesis Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

entri rhiteture he most wellEknown exmple is the esl eutopilot tht n operte on motorwysF pigure PFQ

	2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing 2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing
	the mnner vehiles re ontrolledF hilst intelligent vehile n e designed to e ontrolled volved in the nvigtion tskD di'erent rhiteture designs n e hosenF hese n e lssi(ed 2.2.1.2 Driver Centric
	only y omputer or to ene(t of ommunition with other vehiles nd the infrstrutureD in three tegoriesX vehile entriD driver entri nd network entri QQF
	sitution understnding nd nvigtion system re similrF o filitte the deployment of intelligent vehilesD driver n keep ertin degree of ontrol
	2 Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing sn this ghpterD the nvigtion rhiteture for n intelligent vehile is presented in the (rst on the nvigtion systemF he driver is expeted to perform some nvigtion tsks @eFgF t rod 2.2.1.1 Vehicle Centric intersetion rossingD prking FFFAD to monitor the system or to t s fllEk solutionF his setionD then the driver9s ehviour estimtion to deide to ross n intersetion is studiedF his is followed y study of the deisionEmking proess with regrds to the funtionl rhiteture of n intelligent vehileF sn order to deideD the system requires to uild digitl representtion of the environment to rhiteture requires driver monitoringD to hek if the sitution enountered is understood nd e vehile entri rhiteture implies tht humns re outside the ontrol loopD tht is the infer if the driver is redy to tke ontrol kF st implies tht deisions re needed to determine vehile is under full omputer ontrolF he system uilds digitl model of the environment y when the driver is needed nd whether or not he is redy to tF pigure PFP shows typil fusing dt from emedded exteroeptive sensors s well s Epriori knowledge from mps nd modelsF his type of rhitetureD whilst it n use when ville informtion from wireless driver entri rhitetureF
	understnd its situtionF et rod intersetionD it is neessry to understnd wht other rod ommunitionsD it does not depend on itF pigure PFI shows typil vehile entri rhitetureF Human
	users intend to doD nd possile intertionsF his is possile pplying proilisti grphs nd lssi(tion methodsF he former uses strems of mesurements to infer the driver9s ehviourF lerning to infer the longitudinl nd lterl ehviour of driversF xvigtion form derstnding V ehicle gontroller ehile lteE itution unE he seond setion disussed these pprohes nd provides the rtionle for hoosing mhine ehile tte he ltter uses fetures from the mesurement strem nd its similrity with previous knowledgeF V ehicle hriver wonitorE rumn hriver ing
	ereption
	yver the yersD di'erent pprohes hve een developed for deisionEmking pplied to intelliE
	2.3 Behaviour Understanding . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Denitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ehile tte gent vehilesF snitil endevours relied on (nite stte mhines to determine tions required y the sitution plus trjetory plnner to drive the vehile UF roweverD these required preise invironment xvigtion ehile lteE itution unE gontroller derstnding form representtion of the environment nd when fored with omplex sitution did not sle PPF ereption
	roilisti resoning llows the unertinty of n informtion to e represented nd used for pigure PFI ehile entri rhiteture 2.3.2 Methods for Behaviour Understanding at Road Intersections . . . . . . deisionEmkingF st hs suessfully een pplied to mny driving senrios PVD PWD QHF st lso 2.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . requires humn knowledge to uild prts of the modelsF his hs een improved using mhine 2.4 Approaches to Decision-Making at Road Intersection . . . . . . . . por severl developments of eh levelER vehiles @eFgF ymoAD it is the preferred eh rhitetureF invironment lerning pproh to lern models from dt QIF sn most dvne worksD lerning lgorithm 2.4.1 Rule-based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ht is to operte on stndElone sisF roweverD their vlidtion remins hllengingD s mny n lern y itself ll the deisionEproess QPF hese pprohes re presented in setion PFR 2.4.2 Probabilistic Approach: Partially Observable Markov Decision Process . nd rtionl to pply proilisti resoning re disussedF di0ult sfety prolem @eFgF softwre redundnyD mliious ttksA must e overedF pigure PFP hriver
	2.4.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
	hilst there hs een muh progressD multiple hllenges remin IQF e mjor issue is mhine 2.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pereptionF ensor lyout implies tht severl oluded res remin in dense nd urn situE 2.2 Context Formulation: The Navigation Function In Intelligent tionsF he response from sensor is fr from idel due to their ndwidthD resolutionD quisition
	Vehicles rtesD etF elgorithm of mhine lerning methods still result in flse positive nd flse negtiveF
	here is muh unertinty with regrd to the digitl representtion of the environmentF 2.1 Introduction 2.2.1 Classication of Intelligent Vehicle of Architectures purtherD the ehviour of humn entities populting the environment is di0ult to predit nd
	intertions resulting from vehiles deisions must e soilly eptleF enother hllenge hs
	he rossing of rod intersetion strts with the ehviour estimtion the pereived entitiesD he ontrol of n intelligent vehile n e shred etween di'erent entitiesD nmely the driverD emergedD the pility of qulifying the system performnes in order to gurntee its ehviour
	nd then deiding wht tions to tkeF he pprohes tken for these funtions depend on the vehile nd through the ommunition networkF egrding the emphsis on whom is inE nd sfetyF
	QI QP QQ

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Context Formulation: The Navigation Function In Intelligent Vehicles 2.2.1 Classication of Intelligent Vehicle of Architectures . . . . . . . . . . . . 2.2.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Decision-making for Vehicle Centric Architectures . . . . . . . . . . . . 2.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  likely to overE(tD further nlysis of suh systems might prove them unrelile Related Work on Behaviour Estimation and Decision-Making for Road Intersection Crossing heisionEmking proess uses these inputs to reson with intertions etween entities nd risk ssoited with the situtionF gonservtive pprohes sed on rules hve di0ulties to use this informtionF heres mhine lerning relies on trin dtset to (nd models for intertion nd Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

	motion is oserved vi the onEord vehile sensorsF hilst the ltter is dedued projeting these oservtions on top of rhEmpsF 2 unertintiesD proilisti pprohes use model design y humnsF he ltter ws preferred s his hpter presents the method proposed to nlyse driver trjetories when pprohing
	the rod intersetion senrio rules nd dynmis n e desriedF eson for poliy seletion remins ler for humn oserver in these onditionsF sn this thesisD proilisti methods hve een hosen to model the deisionEmking prolem t rod intersetionF st llows to lssify the longitudinl nd lterl mnoeuvresF he hpter is prtitioned into three prtsX he disretistion frmework to ontextulize the motion of the 3 Manoeuvre Classication Applying driverD the pproh developed to infer driver mnoeuvre through funtionl disretistion nd Learned Functional Discretisation results otined y the omintion of these methodsF
	rod intersetionsF he diret modelistion of trnsitionD rewrd nd oservtion funtions ws
	preferred ginst mhine lerning euse some elements of the model @vehile dynmisD tr0 snitillyD surfes tht the vehiles will ross re segmented into two type of zonesF he 9on)it
	rulesA re known nd n e diretly oded into the modelF e stte representtion s vefevre et lF ws hosen to reson with ehviours nd risks SQF sntertions ws implemented s zones9 where the pth of the oserved nd sujet vehile ould overlp nd the 9pprohing 3.1 3.1 Introduction zone9 where the driver motion hnges euse of the rod intersetion rossingF his represenE
	e dtset tht ontins trjetories of drivers pprohing n intersetion nd nnotted with
	lterl nd longitudinl mnoeuvre n e used to trin suh modelsF hese n e otined hen rossing rod intersetion in the presene of other vehilesD understnding the ehviour vi reording of rel vehile sttes or within simultor IHPF yutputs of the ehviour underE of the drivers is fundmentl for the deisionEmking proessF he sptioEtemporl reltionships stnding system remin unertin euse of humn errti tionsF re onsidered y oserving the relevnt vehile motions nd ssoited ontextF he former
	TS TT TU

IHSF rvel time y itself is not good inditorD s it vlues elertion over sfetyF et rod intersetionD there re multiples solution to ross n intersetionF o penlize system euse it hooses slow poliy nd void unneessry risk is wrongF griteri sed on the senrio n provide etter understnding of the system tions nd llows omprisonF st trgets prolemtis senrioD tht developers should fous onF 2.5 Conclusion he vehile nvigtion system is t the ore of ny utonomous vehile rhiteturesF o drive without n overseeing driver or wireless ommunition is omplitedD s the system must reson with mesurement unertinties nd e sfe in ny situtionsF et rod intersetionD the omplexity rises due to intertions etween entities nd priority rulesF nder these onditionsD the ehviourl plnner is the most hllengedD euse it depends on this informtion to evlute poliiesF et this stgeD ojet lists with ttriutes nd rhEmps re used to infer the ontext for ehviour understnding nd deisionEmkingF nderstnding the ehviour of drivers requires to ontextulize the position of its vehile with regrds to the intersetion nd to nlyse its motionF whine lerning methods is preferred for this tskD euse predition of future sttes is lredy prt of the ywh trnsition modelF reltion etween driver mnoeuvresF o provide etter evlution of the system performnes in di'erent on(gurtionsD key performne inditors tht depend on the senrio re proposedF he wide vriety of pprohes to evlute deisionEmking nd the omplexity to reprodue experiment mke omprison di0ult or impossileF here is need for metris nd methods tht etter shows the performnes of eh methodsF 3.2 Functional Discretisation . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Motion Patterns With Gaussian Processes . . . . . . . . . . . . . . . . . 3.2.2 Functional Discretization Framework . . . . . . . . . . . . . . . . . . . . 3.2.3 Discretization Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 HD-Map Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Manoeuvre Classication . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Random Forest Classier . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Random Forest Topology . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Functional Discretization Compared to Rectangular Discretization . . . 3.4.3 Comparison With Baseline Approach . . . . . . . . . . . . . . . . . . . . 3.4.4 Results With the Hybrid Dataset . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ttion is projeted nd stored in rhEmpF henD ndom porest lssi(ers re trined with fetures extrted from vehile trjetories within zone to lssify driver lterl nd longitudiE nl mnoeuvresF he trining phse tkes dvntge of hyrid dtset mde of simulted nd rel trjetoriesF st redues the need to gther nturlisti dt t the rossrodF o onludeD the proposed pproh is pplied nd ompred with seline methods suh s supE port vetor mhine nd other types of disretistionF hese results re disussed to demonstrte the fesiility of the pproh nd to nlyse its performneF

  5.3.6 Perspectives on Key Performance Indicatorshe list of uss de(ned for the evlution of the proposed deisionEmking frmework is sumE mrised in le SFPF hese re formulted s onditions on mesurementsF e more formlised mnner to de(ne them is presented in etion SFRFRD where the trvel timeD sfe stop nd unE

			le SFP gondition of suess using proposed uss
				enrio e @the yields nd the y rossesA
	g	us	gomfort	rust	fe stop	nsfe stop	rvel time
	uessful vower thn 2ms -3	yver Rs	xone	xone	E
	eeptle	E	E	vower thn 3s	xone	E
	piled	yver thn 2ms -3 vower thn Rs	yver Qs	yver Hs	yver 20s
			enrio f @the rosses nd the y stopsA nd g @ the rosses nd the y yieldsA
	uessful vower thn 2ms -3	yver Rs	xone	xone	E
	eeptle	E	E	vower thn 5s	xone	E
	piled	yver thn 2ms -3 vower thn Rs	yver Ss	yver Hs	yver ISs
	sfe stop uss re reEformulted y using finry viner emporl logi to e nlysed with
	sttistil model hekingF
	he proposed list of uss is not exhustiveF wore group nd uss re requiredD if more omplex
	senrios re studiedD for exmple the intertion with pedestrins or if other system omponents
	need to e nlysedF eentlyD the integrtion of e s soil gent is studied nd ould rete
	new group of uss IRTF uss n lso relte to the di'erent mnner tht drivers ehve
	ross the worldF his should led to the dpttion of the uss to the testing ontextF
	vimits for eh us re found y using expert knowledge or the highwy odeF por someD it ws
	more di0ult to determine n pproprite ound @eFgF fe topA s no rules existF yservtions
	from tr0 my e used to (nd vlue for these oundsF roweverD to opy humn ehviours
	my not e n dequte trget for vlidtion of utonomous systemD s humns understnd the
	highwy ode di'erentlyD s solutions re still eing developedD further mhines perhps need
	their own odesF

  tions ruptlyF purthermoreD fter the rosses the intersetionD it elertes so s to get wy from the unsfe reF sf this tion is preeded y strong deelertionD the exeution fils in term of omfortF his n e oserved in the seond exmple disussed in etion SFRFPF he on(gurtion I performne re t lest UU7 of suess for eh senrioD wheres the on(gurtion P is more dpted to senrio e with VI7 hne of suessF he min resons of filures for the seond on(gurtion in senrio f nd g is triggered y the unsfe stops nd the trvel time ussF st shows tht this on(gurtion is likely slower thn the otherF pon investigtionD some of the unsfe stops re due to the vehile trying to rwl into the intersetionF ht is to stop just fter the intersetion entrne euse the y is trying to rossF he min dvntge of the (rst on(gurtion is tht n ross every time the intersetion @no filure relted with trvel timeAF his on(gurtion is likely to e fster thn the on(gurtion PD however this speed is likely to use some filures in the senrio eD euse it requires the to slow downF snformtion otined on the ehviour of eh system llows for their enhnementF por exmpleD the (rst must void stopping in n unsfe mnnerD wheres for the seond on(gurtionD the intertion etween the nd the y should e improved in order to inrese the time gp perform wgD one needs to e le to qenerte tres of the exeution of the system to vlidteF hese tres hve to e generE ted ording to the proilities in the modelF his is done s prt of the test exeutionF rite the property to hek s formul tht n e deided on (nite treD nd proedure for deiding whether tre stis(es the propertyF sn order to express suh formulsD founded viner emporl vogi @fvvA is usedF his is ounded version of viner temporl logi ISHF sn forml veri(tion it llows to stte onditions tht will eventully e trueF he syntx of fvv is s followsX φ::= p | φ ∨ φ | ¬φ | φ U ≤t φ |X ≤t φF he symol p represents predite expressed on the urrent stteD for instne omprison etween metri nd oundF he disjuntion @∨A nd the negtion @¬A re de(ned s usulF he temporl opertors until @U A nd next @XA de(ne properties out the timeF o deide whether property holds on (nite treD these opertors re prmeterised y time ound t ∈ RF he formul X ≤t φ is true if φ is true in the stte rehed fter t units of time from the urrent stteF he formul φ 1 U ≤t φ 2 is true if IA the formul φ 2 eomes true efore t units of time from the urrent stte nd PA the formul φ 1 remins true in every stte efore the one where φ 2 eomes trueF por forml de(nition of fvv semntisD see ISIF sn prtieD we often use the always @GA nd eventually @F A opertorsF iventully is de(ned

	le SFR erentge of suessful tests nd reson for filure out VHH experiments for eh onE (gurtion from SFQF he yields nd the y rosses for the enrio eF he rosses nd the y stops in senrio fF he rosses nd the y yields in senrio gF wetri ystem under test e f g uess rte gon(gurtion I UU7 UU7 VT7 gon(gurtion P VI7 RU7 ST7 imultion model P7 US7 RP7 fe stop @eeptleA I H PI7 IW7 P T7 IW7 PI7 imultion WI7 H IH7 fe stop @piledA I H H H P H I7 I7 imultion H H H rvel time I H H H P H RT7 QV7 imultion H H H gomfort I IV7 UQ7 TS7 P PT7 IW7 IW7 imultion IV7 H P7 qp I VI7 H H P TH7 I7 H imultion properties re sed on the us nd the tre is the reording of the environment stte during T7 IHH7 VW7 to hnge its usF 5.4.4 Statistical Model Checking ttistil wodel gheking @wgA IRVD IRW provides n intermedite etween test nd exhusE tive veri(tion y relying on sttistisF sn order to perform wgD one needs n exeutle model nd property to hekF he exeutle model is expeted to e stohstiD tht isD to hve some of its trnsitions governed y proilisti hoiesF wost eg simultions re lredy modelled s stohsti proessesD euse vritions in the senrio re de(ned y proility distriutionsF he property to hek must e deidle on (nite treF sn our pplitionD the xme hesription nit t imeEstmp or time elpsed s sstops xumer of stops in the sfe re usstops xumer of stops in the unsfe re tsstops hurtion of stops in the sfe re s tusstops hurtion of stops in the unsfe re s rossed rue if intersetion is rossed sn order to on(gurtion otined in the previous setionF le SFS vist of metris extrted from the test frmework
	n exeutionF he trvel time n e stted sX	I	IH7 IW7 PQ7
	nsfe top	P	IQ7 TU7 TI7
	imultion he exeution eing stohstiD some tres will stisfy the property to hek nd some other I7 H H
	will notF hereforeD the proility tht tre stis(es property n e determinedF he min gol of wg is to evlute tht proilityF his gives more informtion thn yesEorEno F ≤ d crossed
	nswer s it ws relised in the previous suEsetionF sndeedD if the model does not stisfy the with d the mximum durtionF his mens tht eventullyD fter ertin durtionD the interseE
	formulD there is n evlution of how well it performsF tion is rossedF he durtion is hnged to evlute how fst the tested system n rossF
		ISV ISW ITH ITI

s F ≤t φ = true U ≤t φ nd mens tht the formul φ should eome true efore t units of time hppenF elwys is de(ned s G ≤t φ = ¬F ≤t ¬φ nd mens tht φ must lwys hold for the next t units of timeF ith this syntxD uss n e reformulted to e used y the wgF he le SFS shows the vrile tht re extrted from tre to uild the fvv sttementF o show wht ould e lerned with this pprohD three uss re reformulted nd studied with the wgF hese uss re sfe stopD unsfe stop nd trvel timeF he method is pplied to the tres of the (rst

https://www.avsimulation.fr/ IRP

Article R415-2 of the French highway code IRV

Article R415-6 of the French highway code ISI

The special case with γ = 1, that corresponds to no time penalty, is handled with a stop condition that stops the search after 40 iterations ISP

IHR wF ren'D eF gnziniD nd F vegunD wodelEreditive oliy verning with nerE tinty egulriztion for hriving in hense r0D arXiv e-printsD pF rivXIWHIFHPUHSD tnF PHIWF TS IHS hF emodeiD gF ylhD tF teinhrdtD F pF ghristinoD tF hulmnD nd hF wnéD gonrete prolems in es sfetyD ArXiv e-printsD PHITF TSD IRU IHT gF y nd gF vugierD wodelling smooth pths using gussin proessesD in Proc. of the Int. Conf. on Field and Service RoboticsD @ghmonixD prneAD PHHUF UID UPD UQ IHU F fsuD hF fon(liD tF qillD hF uirsnovD nd hF ngD wpping rods through deep lerning nd wekly supervised triningF httpsXGGiFfeookFomGlogGmppingErodsE throughEdeepElerningEndEweklyEsupervisedEtriningD PHIWF lst essed on PQGHUGIWF UI IHV wF fennewitzD F furgrdD qF gielnikD nd F hrunD verning motion ptterns of people for omplint root motionD The International Journal of Robotics ResearchD volF PRD noF ID ppF QI!RVD PHHSF UID UP IHW F qhhrmni nd F oweisD verning nonliner stohsti dynmis using the generlE ized em lgorithmD xsD IWWVF UI IIH gF iF smussenD Gaussian processes for machine learningF ws ressD PHHTF UP III hF squezD gF vugierD nd F prihrdD snrementl lerning of sttistil motion ptE terns with growing hidden mrkov modelsD in Robotics ResearchD ppF US!VTD pringer ferlin reidelergD PHIIF UQ IIP yF igudD gF lünD nd F doisD ynEline regression lgorithms for lerning mehnil models of rootsX surveyD Robotics and Autonomous SystemsD volF SWD ppF IIIS!IIPWD tuly PHIIF UQ IIQ gF wF fishop et al.D Neural networks for pattern recognitionF yxford university pressD IWWSF UT IIR uF ghellpillD ethinking mps for selfEdrivingF httpsXGGmediumFomGdvyftvevelSGhttpsE mediumEomElyftlevelSErethinkingEmpsEforEselfEdrivingEIRUPRUSVdTD PHIVF lst essed HSGHPGPHIWF VS IVP BIBLIOGRAPHY IIS ptilite SFHFHEetHlD sql funtions referene listF httpXGGwwwFgiEgisFitGgiE sinsGsptiliteEsqlESFHFHFhtmlD PHIVF lst essed HSGHPGPHIWF VT IIT vF freimnD ndom forestsD Machine LearningD volF RSD noF ID ppF S!QPD PHHIF VW IIU F hrpD Implementing Decision Trees and Forests on a GPUD ppF SWS!THVF ferlinD reiE delergX pringer ferlin reidelergD PHHVF VW IIV pF edregosD qF roquuxD eF qrmfortD F wihelD fF hirionD yF qriselD wF flondelD F rettenhoferD F eissD F huourgD et al.D ikitElernX whine lerning in ythonD Journal of Machine Learning ResearchD volF IPD ppF PVPS!PVQHD PHIIF WR IIW vF ummelhrdD eF xegreD nd gF vugierD gonditionl monte rlo dense oupny trkerD in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference onD ppF PRVS!PRWHD ept PHISF IHR IPH F tçiD F hF urnerD nd gF iF smussenD qussin proess hnge point modelsFD in ICMLD ppF WPU!WQRD PHIHF IHS IPI F de feuorpsD F treuelD eF erroustEflondetD pF xshshiiD fF frdiD nd F eE sendeD heisionEmking for utomted vehiles t intersetions dpting humnElike eE hviorD in 2017 IEEE Intelligent Vehicles Symposium (IV)D ppF PIP!PIUD tune PHIUF IHS IPP F vefevreD Risk estimation at road intersections for connected vehicle safety applicationsF hesesD niversité de qrenoleD 5yt5 PHIPF IIRD ITV IPQ hF F wyneD tF fF wlingsD gF F oD nd F yF okertD gonstrined model preditive ontrolX tility nd optimlityD AutomaticaD volF QTD noF TD ppF UVW!VIRD PHHHF IIS IPR wF eF olltshekD eF olusD nd wF vivnehD e deision model for gp eptne nd pity t intersetionsD Transportation Research Part B: MethodologicalD volF QTD noF UD ppF TRW ! TTQD PHHPF IIV IPS wF fnslD eF urizhevskyD nd eF ygleD ghu'eurnetX verning to drive y imitting the est nd synthesizing the worstD PHIVF IPH IPT F rnD tF tingD nd F yzgunerD hriving intention reognition nd lne hnge predition on the highwyD in Intelligent Vehicles Symposium (IV), 2019 IEEED tune PHIWF IPH IVQ BIBLIOGRAPHY IPU uF i nd tF ghungD xonliner rke ontrol for vehile wG systemsD IEEE/ASME Transactions on MechatronicsD volF TD ppF IU!PSD wr PHHIF IPQD ISH IPV tF wrtinez nd gF gnudsEdeEitD e sfe longitudinl ontrol for dptive ruise ontrol nd stopEndEgo senriosD IEEE Transactions on Control Systems TechnologyD volF ISD ppF PRT!PSVD wrh PHHUF IPQD ISH IPW uF pitzptrikD qps epted t stopEontrolled intersetionsD Transportation Research RecordD volF IQHQD noF IID ppF IHQ!IIPD IWWIF IPRD IRV IQH eF omniD xF eD hF rsuD nd F F veeD hespotX ynline pomdp plnning with regulrE iztionD in Advances in Neural Information Processing Systems 26D ppF IUUP!IUVHD gurrn essoitesD snFD PHIQF IPW IQI rF 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hshuD yn forml model of sfe nd slle selfEdriving rsD arXiv preprint arXiv:1708.06374D PHIUF IQUD IRT IQV wF frierD eF enzgliD tF uileufD vF ummelhrdD eF igwrD gF vugierD eF vegyD tF sñezEquzmánD nd yF imoninD lidtion of ereption nd heisionEwking ysE tems for eutonomous hriving vi ttistil wodel ghekingD in IV 2019 -30th IEEE Intelligent Vehicles SymposiumD @risD prneAD ppF I!VD siiiD tune PHIWF IQU IVR BIBLIOGRAPHY IQW iqeD egsusF https://www.pegasusprojekt.de/en/F lst essed HPGPHIVF IQU IRH F fokD wF wurerD nd qF prerD lidtion of the vehile in the loop @vilAY mileE stone for the simultion of driver ssistne systemsD in 2007 IEEE Intelligent Vehicles SymposiumD ppF TIP!TIUD tune PHHUF IQU IRI xreD e frmework for utomted driving system testle ses nd senriosD PHIVF vst essed on HVGHVGIWF IQV IRP eF froggiD wF fuzzoniD F hettistiD F qrisleriD wF gF vghiD F wediiD nd F ersriD ixtensive tests of utonomous driving tehnologiesD IEEE Transactions on Intelligent Transportation SystemsD volF IRD ppF IRHQ!IRISD ept PHIQF IQW IRQ F uoopmn nd wF gnerD ghllenges in utonomous vehile testing nd vlidtionD SAE International Journal of Transportation SafetyD volF RD noF ID ppF IS!PRD PHITF IQW IRR inleEQD lidtion nd testing of omplex utomted systemsF https://www. enable-s3.eu/F lst essed HWGPHIWF IRI IRS F witrD eF ghoudhuryD F F eprowD qF uulndiveluD nd tF huwelsD owrds modE eling of pereption errors in utonomous vehilesD in 2018 21st International Conference on Intelligent Transportation Systems (ITSC)D ppF QHPR!QHPWD xov PHIVF IRP IRT iF inkhuyzen nd wF gefkinD heveloping soilly eptle utonomous vehilesD Ethnographic Praxis in Industry Conference ProceedingsD volF PHITD noF ID ppF SPP!SQRD PHITF IRVD ISI IRU qF de gmposD F ploneD nd tF joergD eutonomous oopertive drivingX e veloityE sed negotition pproh for intersetion rossingD in Intelligent Transportation Systems -(ITSC), 2013 16th International IEEE Conference onD ppF IRST!IRTID yt PHIQF IRV IRV F rérultD F vssigneD pF wgnietteD nd F eyronnetD epproximte proilisti model hekingD in Proceedings of the 5th International Conference on Verication, Model Checking, and Abstract ImplementationsD volF PWQU of Lecture Notes in Computer ScienceD ppF UQ!VRD pringer ferlin reidelergD PHHRF ITH IRW uF enD wF iswnthnD nd qF eghD yn sttistil model heking of stohsti sysE temsD in Proceedings of the 17th International Conference on Computer Aided Verication @uF itessmi nd F uF jmniD edsFAD volF QSUT of Lecture Notes in Computer ScienceD ppF PTT!PVHD pringer ferlin reidelergD PHHSF ITH IVS BIBLIOGRAPHY ISH eF nueliD he temporl logi of progrmsD in Proc. of the 18th Annual Symposium on Foundations of Computer ScienceD ppF RT!SUD siii gomputer oietyD IWUUF ITI ISI F uliniD eF ltzerD nd iF wF glrkeD fyesin sttistil model heking with ppliE tion to stte)owGsimulink veri(tionD Formal Methods in System DesignD PHIQF ITI ISP eF ermndD hF pillitD nd tF sñezEquzmánD e yesin frmework for preventive ssisE tne t rod intersetionsD in 2016 IEEE Intelligent Vehicles Symposium (IV)D ppF IIPV! IIQRD tune PHITF ITV ISQ F sishtD hF ufreydzD nd eF plnzniD fuilding prior knowledgeX e mrkov sed pedestrin predition model using urn environmentl dtD in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)D ppF PRU!PSQD xov PHIVF ITW ISR fF olzD rF wielenzD sF qilitshenskiD F iegwrtD nd tF xietoD snferring pedestrin motions t urn rosswlksD IEEE Transactions on Intelligent Transportation SystemsD

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