Eric Proust

Harold P Gerrish

2033 ''Fast conjunction'' Long Stay Mars Mission Mission Total Outbound Inbound

Earth Mars Moon Space Annual Total 3 mSv 245 mSv 438 mSv 657 mSv Daily Average 8.2 10 -3 mSv 0.67 mSv 1.2 mSv 1.8 mSv Annual Ambient Radiation Levels for the Earth, Mars and Space* Manned Mission To Mars: ΔV budget vs. Transit Time / Radiation Dose

The US Rover/NERVA program (1956)(1957)(1958)(1959)(1960)(1961)(1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972): 20 NTP rocket reactors designed, built and ground tested "Demonstrated all the requirements needed for a viable lunar space transportation system as well as for human Mars exploration missions" "Achieved a TRL ~6" The Small Nuclear Rocket Engine Design Concept (NERVA, 1972) The CEA-CNES MAPS Study Program (1994-1997) u Goal: develop the knowledge required to decide whether to initiate an R&D program on NTP The CEA-CNES MAPS study program 1994-1996 Regulators focused on the confidence, or lack thereof, in system dynamics and the potential to melt fuel (Cf. DOE regulators: Duff/Krusty) ð A circular dilemma for NTR systems:

• complex, unknown reactor dynamics and control issues that only be solved via nuclear system testing (temperature reactivity feedbacks: thermal expansion & XSs; H 2 reactivity worth; peaking factors; coupling between H 2 pressure/ flow rate and power through turbopump; requirement for very quickly reaching full power after the onset of H 2 flow) • without in-hand solutions to these issues, there may be no ability to get approval for and successfully execute the tests ð Make the system as simple as possible in terms of system dynamics and controllability

The Challenges of Nowadays Testing Nuclear Thermal Propulsion Engines 35 NTRs combine very small temperature margins and very high power densities Average adiabatic heat-up rates: ~500 to 2000 K/s (25 to 100 times higher than a typical PWR) ðIf cooling is lost during powered operation, an NTR core would melt within seconds Decay power also a significant concern; one hour after shutdown the adiabatic heat-up rate can still be >1 K/s

Ground testing NTR engines ð In-space testing instead??

ð To build a facility that captures engine exhaust and guarantees containment for all credible core dispersion scenarii Qualifying this facility might be harder than qualifying the NTR itself: NTR safety concerns, compounded by a high volume of combustible hydrogen, are orders-of-magnitude beyond any reactor safety approval that has been attempted in decades If NTP is so good, why hasn't it happened? (a few quotes)

37

A chicken and egg syndrome?

u ''It takes longer to develop an NTP system than to develop a space mission. Project managers cannot include NTP systems in mission planning until system has been developed and tested'' The main focus is on the LEU Cermet fuel option (potential for better fission products retention compared with NERVA-type fuels)

On the other hand, a practical reactor plant can be distinguished by the following characteristics: (1) It is being built now (2) It is behind schedule (3) It is requiring an immense amount of development on apparently trivial items. Corrosion, in particular, is a problem (4) It is very expensive (5) It takes a long time to build because of the engineering development problems (6) It is large (7) It is heavy (8) It is complicated

The tools of the academic-reactor designer are a piece of paper and a pencil with an eraser. If a mistake is made, it can always be erased and changed. If a practical-reactor designer errs, he wears the mistake around his neck ; it cannot be erased. Everyone can see it. Fuel nanoparticles (< 100 nm):

-high probability of fission fragment escape -high heat radiation efficiency (large surface to volume ratio)

Dusty plasma cloud:

-fuel nanoparticles: E/q = 10 -5 eV/q, 10 5 amu/e -fission fragments: E/q = 10 3 eV/q, 5 amu/e ð fuel particles electrostatically or magnetically contained within the reactor core ð fission fragments magnetically extracted for thrust and/or power generation Radiation levels vs Career exposure limits for NASA astronauts

The NASA astronaut career depth equivalent dose limit is based upon a maximum 3% lifetime excess risk of cancer mortality In 1986, new U.S. NRC regulation, 10 CFR 50.64, which places limitations on the use of HEU in nonpower reactors: "The Commission will not issue a construction permit after March 27, 1986 for a non-power reactor where the applicant proposes to use highly enriched uranium (HEU) fuel, unless the applicant demonstrates that the proposed reactor will have a unique purpose'' (= ''a project, program, or commercial activity which cannot reasonably be accomplished without the use of HEU fuel'')

The current U.S. policy on the use of HEU in reactor systems endorses the use within naval vessels. There is currently no U.S. policy on the use of HEU in space nuclear reactors.

The use of HEU in highly specialized systems such as space power reactors and propulsion systems must be balanced with the potential risks associated with the proposed mission Minimization and, to the extent possible, elimination of the use of HEU in civil nuclear applications:

In the US, eight civilian research and test reactors continue to use HEU since an alternative fuel has not yet been developed for their conversion.

  Way" Transit Time to and from Mars (days) 2033 ''Fast conjunction'' Long Stay Mars Mission Space Propulsion: another example of the benefits of a twice higher Isp (2/2) \]\$ ^\]_`a Dbo p q6r Mini : initial total mass of spacecraft in LEO (= Spacecraft mass + Payload mass + propellant mass) \]\$ : Source (Delta V): Borowski et al., Space 2013, AIAA-2013-5354 Mission mass ratios vs. transit time to Mars Reminder: average space radiation level ~1.85 mSv/day Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Nuclear Thermal Propulsion (NTP): enabler of manned missions to Mars? 13 Source: B. G. Drake "Human Mars Mission Definition: Requirements & Issues", Human 2 Mars Summit, 2013 Electric Propulsion: SEP: Solar NEP: Nuclear 2033 -2045 opportunities For an detailed explanation of why, although EP has a much higher Isp, NTP outperforms NEP (and SEP), wait for my next lecture on Space Fission Power and Electric Propulsion The short explanation: electric thrusters have a very (very) low thrust* and they need a power supply * See back-up slide Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

  20-100 W/(m K) depending on temperatures Thermal properties of nuclear thermal rocket Carbide fuel options Operating time vs coolant exit Temperature for the full-power NERVA/Rover reactor tests Source: Experience gained from the Space Nuclear Rocket Program (Rover), D. R. Koenig, LA-10062-H UC-33, May 1986 Composite fuel: 35 vol% (U, Zr)C web 15 Vol% void graphite matrix U: 0.64 g/cm 3

Project

  Rover/NERVA shut down in 1973 (Nixon): loss of interest of the public for human space flight, end of space race, growing use of low-cost unmanned robotic space probes, budget cuts due to cost of Vietnam war … Source: Experience gained from the Space Nuclear Rocket Program (Rover), D. R. Koenig, LA-10062-H UC-33, May 1986 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

Fuel

  Engine Definition Study Preliminary Report, Volume 1 -Engine Description, LA-5044-MS, 1972 21 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Fuel assembly (6-8 bundles stacked) Twisted ribbon fuel element (mm) -to-volume 2.6 times higher than prismatic NERVA fuel) Ternary carbides (U ≤ 2.5 g/cm 3 ) UC-ZrC-C (< 2500K) UC-ZrC-NbC (up to 3100K), RD-0140 (196 MW/35 kN, 910 s Isp) Propellant: H 2 + Hexane Core outlet T: 3000 K Core: φ 0.5 x H 0.8 m 2 Engine: φ 1.2 x L 3.7 m 2 Engine mass ! 2 000 kg Source: Zakirov, Vadim, and Vladimir Pavshook. Russian Nuclear Rocket Engine Design for Mars Exploration. Rep. no. 1007-0214. N.p.: TSINGHUA SCIENCE AND TECHNOLOGY, June 2007 An overall effort comparable with the US NERVA program, Also carbide fuel, however a quite different design approach with high temperatures localized to fuel bundles: • simplifies design of the rest of the core which operates at much lower temperatures (moderator, core support structures, including downstream support plate) • enables radial and hydraulic profiling • simplifies nuclear testing (enables H 2 irradiation loop testing of FA in research reactor: no need for whole core testing to assess nuclear performances like in NERVA) An overall effort comparable with the US NERVA program, A quite different design approach Stacked fuel bundles: • possibility of axial profiling / axial variation of fuel composition (UC-ZrC-C upstream, UC-ZrC-NbC downstream) Twisted ribbon fuel: • fuel surface-to-volume 2.6 times higher than prismatic Power cycle duration (1200 K): 6000 hrs Source: S.K. Bhattacharyya, An assessment of fuels for nuclear thermal propulsion, ANL/TD/TM01-22, 2001 An overall effort comparable with the US NERVA program, A quite different design approach USSR NTP Program (~1960-1989) limits of Non-nuclear hot H 2 Testing Nuclear test facilities (Semipalatinsk site): • IGR reactor (5 s power pulses in hot H2 loop) • EWG-1 reactor (230 MW, flowing H 2 , multiple NTP FAs) • IRGIT reactor (prototype NTP reactor, designed for 3000 K outlet K, tests run up to 270 MW, converted in the early 80's to the RA reactor test facility for investigating FP deposition) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Nuclear Fuels for Nuclear Thermal Propulsion: Beyond Carbides, Cermet Fuels 25 Nuclear Fuel Design Issues: • Material evaporation, • Melting temperature, • Thermal conductivity, • High temperature chemical stability, • Corrosion/mass loss in flowing H 2, • Fission product release, • Uranium density, • Reactor neutron spectrum, • Fabrication, • Fuel swelling, • Thermal expansion mismatch with coating/cladding • Thermal shock resistance, • Mass density, • …Properties of nuclear thermal rocket fuel options ( * ) W-UO2 Cermet: 60 v% (10m% GdO1.5-stabilized UO2) (£) (U, Zr)C as tested in NF-1 ($) (U, Zr)C, C as tested in NF-1 (a) depending on temperatures Room T -High T Sources incl. L. B. Lundberg & R.R. Hobbins, Nuclear Fuels For Very High Temperature Applications, 27th IECEC (1992) EGG-M--92067 W: the only known fully stable material in flowing H 2 at T> 2500K ð W-based Cermet fuel Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

  U P: 510 MWth; Active core Vol: 66 dm 3 ; Fuel power density: 9.4 GW/m 3 Max Outer/Inner Core Fuel T: 2010 / 2880 K, Chamber T: 2670 K, Isp: 900 s Engine Mass: 2500 kg (incl. 115 kg internal shield) Thrust to Weight ratio: 5.3 Sources incl. Stephen D. Peery et al., XNR2000 --A Near Term Nuclear Thermal Rocket Concept, AIP Conference Proceedings 271, 1743 (1993); Randy C. Parsley, Advanced Propulsion Engine Assessment based on a Cermet Reactor, Nuclear Propulsion Technical Interchange Meeting, NASA Lewis Research Center, October 20-23, 1992 (NASA-CP-10116, Vol. I, pp 150-216); K. O. Westerman et al., Babcock & Wilcox Assessment of the Pratt & Whitney XNR2000, NASA-CP-10116, Vol. I, pp 217-245) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) CERMET vs. Graphite-Based Nuclear Fuels for Nuclear Thermal Propulsion Engines 29 ''Historic'' CERMET Fuel (W-UO 2 ) Rocket Reactor Concepts: J Better fission products retention J Long operating life with multiple restarts and temperature cycling (thermal shock resistance, ductility, strength) • Fast spectrum cores: J Simpler design (no moderator to cool, simpler core support) K Much more compact core (than with thermal spectrum) J Negligible Xenon reactivity effect ( * ) , no hydrogen reactivity feedback (negligible reactivity worth, important for startup with cold H2) J Intrinsic ''neutronics spectral shift effect'' ensures reactor subcriticality in the event of a water immersion accident, idem compaction L Inherently higher 235 U mass (x ~3) than thermal spectrum reactors K Much higher fuel density (offsets compactness) L Lack of nuclear power reactor tests L Relies on HEU (criticality-limited): a LEU CERMET fast spectrum core would be prohibitively large/massive A (moderated) Cermet-fueled LEU core would require using W 95 w% enriched in 184 W, enriched Re due to the large absorption XS of natural W (30% 184 W) and rhenium in a thermal spectrum. UO 2 stabilizer Gd 2 O 3 will have to be replaced by ThO 2 Absorption XS of stable W isotopes ( * ) see back-up slide Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

u

  (paper) Study included -Assessment of engine performances (mean Isp, mass, operating constrainsts, reliability, recurring cost, …) -Safety evaluations -Proposal of an R&D Program u Design Strategy -Rely AFAP on off the shelves or near term technologies -While offering prospects for performance improvements with more advanced technlogies u Mission as study framework: -5 round-trip cargo missions from LEO to moon orbit -Launched with ARIANE V -Payload with its H 2 for a one-way journey launched separately Choice of a particle-bed reactor design concept* with Beryllium as moderator/reflector Sources: Raepsaet, X., E. Proust, et al. (1995), "Preliminary Investigations on a NTP Cargo Shuttle for Earth to Moon Orbit Payload Transfer Based on a Particle Bed Reactor," AlP Conference Proceedings No. 324, 1: 401-408; R. Lenain et al., Conceptual design of the French MAPS NTP cargo shuttle based on a particle bed reactor, AIP Conference Proceedings 361, 1169 (1996) * See back-up slides Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

  , X., E. Proust, et al. (1995), "Preliminary Investigations on a NTP Cargo Shuttle for Earth to Moon Orbit Payload Transfer Based on a Particle Bed Reactor," AlP Conference Proceedings No. 324, 1: 401-408; R. Lenain et al., Conceptual design of the French MAPS NTP cargo shuttle based on a particle bed reactor, AIP Conference Proceedings 361, 1169 (1996) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Space Nuclear Reactors: Safety Principles Use only fresh Uranium as fuel $ (reactor launched free of fission products); Use of Plutonium precluded Reactor designed to prevent accidental criticality whatever the emergency situation (in case of reactor compaction and/or flooding upon impact following launch abort, …) First criticality and operation started only once prescribed ''sufficiently high orbit''* reached ("nuclear safe" orbit, allowing for sufficient FP radioactive decay before reentry) Minimize fission product release (principle ALARA) Reactor designed either survive accidental reentry or to disintegrate upon reentry and disperse its residual radioactivity in the upper atmosphere (soviet strategy adopted in the latest RORSATs, Cf. 1983: Kosmos 1402) Safety objectives and regulations are currently established on the basis of national political/legal rules: USA, Russia (Europe?) List of internationally agreed-upon principles (UN Committee on the Peaceful Uses of Outer Space, 1992) but no specified safety criteria or regulations so far "Space Nuclear Safety Culture" inspired from the experience learned from past "nuclear launches" Source: Principles relevant to the use of Nuclear Power Sources in Outer Space. Report of the Committee on the Peaceful Uses of Outer Space, General Assembly Official Records Forty-seventh Session. Supplement No. 20(A/47/4/20) u Engine start-up and shut-down transients have little impact on engine performances : ~5% degradation of I sp Safety aspects u Normal operation -Low fluence (3 10 19 n/cm 2 ) / low burn-up (< 0,2% FIFA) / short duration of high temperature operation (< 6000 s) -Fission products inventory < 1 MCi, down to <1 kCi after 1 month -Even if 100% release of FP during ΔV for leaving LEO (over 7000 km), small radiation impact compared to natural space radiation background u Accidents during operation -Low decay heat, <1 m 3 high pressure H 2 tanks sufficient (loss of turbopump) -Passive operation on main H 2 tank pressurization provides some residual manoeuvring capability (> 4 kN thrust at 490 s Isp) to avoind re entry u Launch abort / Re-entry issue -Launch abort: subcriticallity ensured in case of flooding (B 4 C chains, Gd wires), structure likely ideal to prevent criticality-leading reconfiguration -Operation beyond a 600 km circular orbit (11 y lifetime = < 100 Ci re entry) Sources: Raepsaet, X., E. Proust, et al. (1995), "Preliminary Investigations on a NTP Cargo Shuttle for Earth to Moon Orbit Payload Transfer Based on a Particle Bed Reactor," AlP Conference Proceedings No. 324, 1: 401-408; R. Lenain et al., Conceptual design of the French MAPS NTP cargo shuttle based on a particle bed reactor, AIP Conference Proceedings 361, 1169 (1996) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) The CEA-CNES MAPS Study Program 1994-1997 34 u Recurring cost estimate: 20 M US$ 1996 Development and ground testing approach: u Small thrust engines (cluster them if need be) -minimize ground demonstration costs (reactor/engine tests require exhaust capture!) … flight demonstration -maximize applications (moderate and high thrusts) u Design aiming at minimizing RD&D costs -Scalable design -Modular design enabling nuclear testing of individual fuel assemblies -Design enabling pertinent non-nuclear high temperature fuel and fuel element testing in stagnant and flowing H 2 Engine Exhaust Capture Process Scheme (NASA) Source: Low Enriched Uranium (LEU) Nuclear Thermal Propulsion: System Overview and Ground Test Strategy, David Coote (NASA/SCC), JANNAF Programmatic and Industrial Base Testing and Evaluation Technical Interchange Meeting, Nov 8, 2017 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

  Source: Considerations Inspired from David I. Poston, Nuclear Testing and Safety Comparison of Nuclear Thermal Rocket Concepts, ANS NETS 2018; Roy Reider, KIWI-TNT ''explosion'', LANL Report LA-3351 UC-30, 1965 Kiwi TNT (1965) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Nuclear Thermal Propulsion: Beyond Solid Core Concepts Towards Higher Isp 36 Liquid Core Nuclear Rocket Open-Cycle Gas Core Nuclear Rocket Closed-Cycle Gas Core Nuclear Rocket Nuclear Pulse Rockets Other Nuclear Rocket Engine Concepts studied in the 60's (*see ''bonus'' slides) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

u

  "If only reactors could be developed, users would emerge to claim them'' u "NTP ready for flight tests and yet no users have come forward in ensuing decades'' u "NASA was dominated by people who built there life around chemical rockets; they didn't want to see [nukes] come in because they feared it" Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)NTP: having being waiting to go (back) to the moon … and beyond, to Mars?A strong push in the US to switch from ''historic'' HEU use to LEU ( * ) (<20% 235 U)• Non-proliferation policy motivation: US commitment to minimize, and to the extent possible eliminate, the use of HEU in civilian nuclear applications• Prospects from cheaper costs through a commercial development effort (Cf. SpaceX) for which LEU is probably the only option• Additionally,• Avoid the costs of managing the security risks associated with HEU • Avoid the political risks of project cancellation due to controversy over the use of nuclear weapon-grade fuel and facilitate international partnership for space missions with non nuclear weapon states ð A NASA effort to address the key challenges related to determining the technical feasibility and affordability of an LEU-based NTP engine US space policy 2010 Obama's Space Policy Directive(SPD): ''By the mid-2030s, send humans to orbit Mars …'' NASA analysis reconfirms benefits of nuclear thermal propulsion 2017 Trump's SPD 1: emphasis switched to a human exploration program that would return astronauts to the surface of the moon by 2028 and lay the groundwork for a sustained presence, followed by missions to Mars 2019: timeline for the return accelerated to 2024 2020: SPD6 ''The ability to use space nuclear power and propulsion systems safely, securely, and sustainably is vital to maintaining and advancing United States dominance and strategic leadership in space'' ( * ) see back-up slides Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Current US Orientations: Assess the Feasibility of an LEU-based Engine 42 A NASA effort to address the key challenges related to determining the technical feasibility and affordability of an LEU-based NTP engine • Maturing technologies associated with fuel production, fuel element manufacturing and testing • Developing reactor and engine conceptual designs • Performing detailed cost analysis for developing an NTP flight system • Pursuing multiple study paths to evaluate the cost/benefits and route to execute a NTP Flight Demonstration Project

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  * ) assumed managed by orificing each fuel element and each individual flow channel for LEU Cermet study by D. Poston (LANL) Same scale for the 4 reactor cross sections Source: David I. Poston, Design Comparison of Nuclear Thermal Rocket Concepts, ANS NETS 2018 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Impact of Switching from LEU to HEU for NTP: one out of many assessments (2/2) 44 Source: Considerations Inspired from David I. Poston, Nuclear Testing and Safety Comparison of Nuclear Thermal Rocket Concepts, ANS NETS 2018make the system as simple as possible in terms of system dynamics and controllability, so as to reduce challenges in the licensability of ground test facility and so that testing can be closer to a demonstration than an actual test A concept comparison study by D. Poston (LANL)

u

  NTRs = large empty (coolant) volume fraction + lesser reflector/drums worth (large diam. Cores) + difficulty of integrating safety rods ð Gd wires Achieved owing to spectral shift (W, Mo, Gd) + restricted coolant area (but higher P, ΔP) Reactor dynamics & control: Change of peaking factors over lifetime as control drums are moved to compensate ð may prevent use of orificing The robustness/insensitivity to required mission energy (the combination of payload mass and ΔV) offered by NTP can be used to provide flexible mission planning by trading objectives including: -Enabling faster trip time for crew -More payload -Fewer super heavy-lift launch vehicle launches (the launch mass -thus cost-savings over ''all chemical'' or ''chemical+ aerobrake'' for one human mission alone can pay for NTP development/qualification effort) -Enabling off-nominal mission opportunities and wider injection windows -Enabling crew mission abort options not available from other architectures NTP is a safe, affordable "game changing" technology for space propulsion that enables faster trip times and safeguards astronaut health u The biggest challenge facing NTR development: the ability to ultimately perform a successful rocket test -Fuel development, safeguards and launch safety are all major challenges … -… but the ability to create the infrastructure (money, facilities, people) to test, get the approvals to test, and design/engineer a system that actually works is the biggest hurdle. Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Commissariat à l'énergie atomique et aux énergies alternatives -www.cea.fr Thank you for your attention! Any question? Last update: January 2021 Author: Eric PROUST email: firstname.lastname@cea.fr Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) DE LA RECHERCHE À L'INDUSTRIE Commissariat à l'énergie atomique et aux énergies alternatives -www.cea.fr Bonus #1 ''Advanced'' Nuclear Thermal Propulsion Concepts #2 Nuclear Pulse Propulsion Concepts #3 Air-Breathing Nuclear Thermal Propulsion Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) DE LA RECHERCHE À L'INDUSTRIE Commissariat à l'énergie atomique et aux énergies alternatives -www.cea.fr ''Advanced'' Nuclear Thermal Propulsion Concepts Bonus # 1 (6) It is very flexible in purpose ('omnibus reactor') (7) Very little development is required. It will use mostly off-the-shelf components (8) The reactor is in the study phase. It is not being built now A foreword, by USN Admiral Rickover (''the father of the Nuclear Navy''), June 1953 50

  SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) How to Significantly Exceed Past (NERVA) Performances? 51 u Reduce ''molecular'' weight of propellant ð Dissociation of the H 2 propellant u Increase Fuel Temperature ð Liquid (fuel) cores ð Gaseous (fuel) cores u And what about fission fragments as the propellant rather than slowing them down to heat the fuel that will heat the H2 propellant? u Switch from fission controlled chain reaction to explosive reaction (fission pulse propulsion) u Switch from fission to fusion pulse propulsion u Ultimately: antimatter propulsion Engine Performance Trade-offs: High Specific Impulse Isp (high propellant exit velocity/temperature) High Thrust for manned missions (high propellant flow rate) High Thrust to Weight ratio T/W ratio (high power density) ''Advanced'' ''Futuristic'' Concepts Some of them investigated in the 60's in parallel with solid fuel NTP concepts Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Hydrogen Dissociation 52 u H2 Dissociation to provide a low molecular weight propellant u Dissociated H2 recombination in the nozzle to add thermal energy to increase T u Both Maximum theoretical Isp with NERVA-type engine: Isp = (1.41) x NERVA Isp (~900 s) = ~1300 s s V Dissociation insignificant at realizable T chamber for a solid-fuel engine unless P chamber well below the 40 Bars required to achieve high power density in NERVA reactors A Low-Pressure Design? ðLow power, low Thrust and longer burn time A joker?: dissociation/recombination quite significantly increase convective heat transfer ð The LPNTR concept Fission fragments: 169 MeV or ~3% speed of light (out of ~203 MeV released by U235 nucleus fission) To recover > 50% of fission fragments energy: ð sub-micron thick fissile material coating (UC) on micron-scale diameter C fiber ð fibers arranged in layers with ρ.t l < 1 mg/cm 2 ð fast rotation of fiber layers for small residence time in reactor and heat radiation outside ð very low fuel density ~10 -4 g/cm 3 requiring large reactor (reflector) size with highly fissile materials (242*Am: the best but …) Source: George f. Chapline et al., Fission fragment rockets -a potential breakthrough, in Proc. 1988 International Reactor Physics Conference, Vol. 4 ?? Waste heat extraction ? Fuel burn-up ? Radioactive pollution !! Very high Isp but… … very low thrust Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Fission Fragment Propulsion (2/2): Dusty Plasma Based Reactor Concept (2005) 61 Sources: Rodney A. Clark and Robert B. Sheldon, Dusty Plasma Based Fission Fragment Nuclear Reactor, AIAA 2005-4460; Robert Werka et al., Final Report: Concept Assessment of a Fission Fragment Rocket Engine (FFRE) Propelled Spacecraft, 2012

  SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Alpha-Decay Particle Propulsion Sail: a Dead End! (A.A. Bolokine, 1982 patent) Production (and handling) of 232 U !!! Zhang et al., Revisiting alpha decay-based near-light-speed particle propulsion, Applied Radiation and Isotopes 114 (2016) 14-18 ( 232 U daughters 224 Ra, 220 Rn, 212 Bi are strong γ emitters) with 232 U comparable to the one of solar sails near the earth (~9 µN/m 2 ) Transfer some of the neutron energy to ''expellant'' (frozen H 2 surrounding the DT pellet) to increase debris kinetic energy / Isp (typically 30% of neutron energy) and reduce shielding requirements (spatial shaping of expellant can further reduce neutron irradiation of spacecraft components) OE + • ƒ ! + wxŠs+c•8 / "+ ;'xzŽ+c•8 ++ OE ;•xŠy+c•8 ' / % . !"!#$ = sŽ+'zz+',b) . !"!#$ = '+;sz+',b) • • Shield the superconducting coil from neutrons (and X-rays) to avoid quenching and reduce heat load to be extracted by the cryogenic system and radiate the deposited heat to space (typically, 2% of the DT fusion is deposited in the coil shield, requiring a ~500 tons shield) Breed Tritium onboard using the fusion neutrons (through (n, 7Li) reaction in the liquid Li coolant of the superconducting coil shield): transporting the T inventory for the mission (~2 000 kg for a trip to Mars) raises launch safety and cost issues Use a cone-surface-shaped spacecraft to minimize the neutron (+ X-rays) fraction intercepted by the spacecraft structures (2 x 6°or 3% of 360°) Source: C. D. Orth, VISTA -A Vehicle for Interplanetary Space Transport Application Powered by Inertial Confinement Fusion, LLNL report UCRL-TR-110500 (2003) Source: TORY II-C Performance Parameters UCRL-6842-T, 1962

Flow

  The very last development of the US (Manned) Aircraft Nuclear Program (1946-1961, US$ 1950's 24 billions, incl. 2 billions for engine development) Source: Comprehensive Technical Report, GE Direct-Air-Cycle ANP Program, XNJ 140E Nuclear Turbojet, Section 4. Reactor, APEX-908 Part B, May 1962 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Air-Breathing NTP: GE XNJ 140E-1 Nuclear Turbojet Engine (1960-1961) Y 2 O 3 -stabilized ZrO 2 coating) (10.9 cm)

  Source: Comprehensive Technical Report, GE Direct-Air-Cycle ANP Program, XNJ 140E Nuclear Turbojet, Section 4. Reactor, APEX-908 Part B, May 1962 > 1 000 hrs reactor operating lifetime (BeO subject to water-vapor corrosion) Fuel element similar to Tory's Program cancelled before nuclear testing Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) USSR Nuclear Turboreactor Program 88 Annular Shaft-Axis-Symmetrical Nuclear Reactor Off-Shaft-Axis Nuclear Reactor Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

(

  poor) Shielding effectiveness against galactic cosmic radiation at solar minimum Source: L. Joseph Parker, Human radiation exposure tolerance and expected exposure during colonization of the moon and Mars, 2016

  Source: Space Faring -The Radiation Challenge. NASA, EP-2008-08-116-MSFC 93 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Radiation Shielding from Nuclear Thermal Engines Sources include: Space Faring -The Radiation Challenge. NASA, EP-2008-08-116-MSFC; B. D. Taylor et al., Cryogenic Fluid Management Technology Development for Nuclear Thermal Propulsion, AIAA 2015Astronauts and the General Public (in Sv) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Radiation Shielding from Nuclear Thermal Engines 95 External shield mass may reach 50% of (unshielded) engine mass Shielded engine Thrust to Weight ratio (T/W) impacts performance (NERVA-Derived 100 kN ENABLER: T/W=4.8 ( * ) w/o ext. shield; could decrease down to 3.2 with external shield) Sources of graphs include: Javis A. Caffrey, Shielding Development for Nuclear Thermal Propulsion, NETS 2015; H. Ludewig et al., Design of Particle Bed Reactors For The Space Nuclear Thermal Propulsion Program, Progress. In Nuclear. Energy, Vol 30, 1996 2016, 365 Day Mission to Mars with 30 Day Stay ( * ) ( * ) T/W expressed in lbf/lbm (= N/(g 0 kg)) Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) LiH: J the most effective per unit mass: H density 90% of room T water, absorption by 6 Li: σ th =940 barns, 7.5% in Li nat J neutron capture does not emit gammas: J 9 SNAP shadow shield fabricated (cast), developed for SP-100 (cold-pressed) B 4 C: L mass penalty >20% (90% 10 B) up to > 300% (Nat B) compared to ''practical LiH shield'' C density 25% that of graphite; absorption by 10 B: σ th =3800 barns, 20% in B nat J minimal production de secondary gammas by neutron capture ( 10 B(n, α1,γ)) J excellent thermal conductivity and chemical stability, currently fabricated in large quantities L cost of 10 B enrichment K mass reduction by combining B 4 C with Be (neutron moderator) in a multilayer sandwich design Radiation Shielding from Nuclear Thermal Rocket Engines 96 Neutron Shield Materials --/ "+ OE + ••+ sxzŠ+c•8 / + %+Psx•Š+c•8R | ƒ BATH: developed for the internal Shield of NERVA-derived engines (Al 70w%, TiH 1.8 30w%, B 4 C 5w%) L narrow operating temperature range: [600 -800] K and poor thermal conductivity: 4-5 W/mK > 600 K to prevent unacceptable irradiation swelling) < 800 K to prevent unacceptable thermally-induced dissociation/swelling L chemically unstable (pyrophoric) in oxidizing atmospheres and 23% volume expansion at melting Gamma Shield Materials Pb: J the most effective per unit mass (except U); J inexpensive; L 600 K melting point W alloy: J effectiveness per unit mass comparable to Pb, 30% high than Fe; L cost; J high strength at high temperature W + 8% B 4 C (90% 10 B) to reduce secondary gammas: improve mass effectiveness Sources include: The Evaluation of Lithium Hydride for Use in a Space Nuclear Reactor Shield, KAPL, Inc. Report MDO-723-0048, December 9, 2005; Javis A. Caffrey, Shielding Development for Nuclear Thermal Propulsion, NETS 2015 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) The Context and Stakes of Switching from HEU to LEU Fuel for NTP (1/3) 97 Long standing commitment of The United States to eliminate (to the extent possible) the use of HEU in all civilian applications, including in the production of medical radioisotopes, because of its direct significance for potential use in nuclear weapons, acts of nuclear terrorism, or other malevolent purposes The Reduced Enrichment for Research and Test Reactors (RERTR) program, initiated in 1978 by the US DOE: an international effort to support ''the minimization and, to the extent possible, elimination of the use of HEU in civil nuclear applications by working to convert research reactors and radioisotope production processes to the use of LEU fuel and targets throughout the world'' To reduce penalty to switch to LEU, development and qualification of: • existing fuels with increased U density: UAl x -Al dispersion fuel (1.7 to 2.3 gU/cm 3 ), U 3 O 8 -Al dispersion fuel (1.3 to 3.2 gU/cm 3 ), UZrH x alloy fuel (0.5 to 3.7 gU/cm 3 ) • new fuel: U 3 Si 2 -Al dispersion fuel (qualified at 4.8 gU/cm 3 ) Since 1978, more than 70 civilian research reactors have been converted from HEU to LEU (> 20% 235 U) and ~30 additional civil reactors that used HEU have been verified as shutdown. Since 1980, more than 20 large (>1 MW) new research reactors have been designed to use LEU fuel + development of targets and processes for the production of the medical isotope Molybdenum-99 with LEU The political non-proliferation context Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) The Context and Stakes of Switching from HEU to LEU Fuel for NTP (2/3) 98

  ANLCermet-Fuel-based 2000 MW & 200 MW Engines Conceptual Designs (1965)

		ENGINE
		Thrust		445 kN
		Chamber H 2 Pressure		3.6 Mpa
		Chamber H 2 Temperature	2500 K
		Isp		832 s
		Thrust/Weight		~5
		Operating time		up to 10 hours
		Restart capabilities		up to 40
		REACTOR
		Neutron spectrum		Fast
		Power		2000 MW
		FUEL Fuel composition	W-60%UO 2 -6%Gd 2 O 3	200 MW/44.5 kN Engine
		235 U enrichment		93%
		Fuel clad		W-25 Re
		FUEL ELEMENT
		Number		163
		Active length		87 cm
		Across flats		4.75 cm
		Coolant holes number		331
		diameter		1.7 mm
	2000 MW/445 kN Engine	Peak fuel temperature		2728 K
				27
	W-UO 2 Cermet Fuel Cermet-Fuel-Based Engine Concept Revisited: the XNR2000 (Pratt & Whitney, 1993)
	1962-1968 developments GE (710 program) & ANL Nuclear Rocket Program A 111 kN Rocket Engine Material & FE development + critical experiments + NTP conceptual designs Two-Path ''Folded H 2 Flow'' configuration (outer, then inner core)
	Cermet Fuel drawing from GE 710 and ANL program results
	Inner core: W-UO 2 (60 vol% UO 2 , 93% 235 U, 6 mol% Gd 2 O 3 )
	Outer core: Mo-UO 2 (it is ~50% lighter!)
	Fast spectrum (criticality-limited) Core: 270 kg 235
	After 1 h exposure to H 2 at 2610 K
	ð W-UO 2 Cermet: 60 v% (10m% GdO 1.5 -stabilized UO 2 )
	Promise for high fission product retention		2000 MW Engine FE 331 coolant holes
				710 Program FE 37 coolant holes

Sources incl. ANL Nuclear Rocket Program Quarterly Progress Report Fourth Quarter 1965 ANL-7150; 710 High-Temperature Gas Reactor Program Summary Report, GEMP-600, 1968 710 Critical Mock-Up Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021) Sources incl. ANL Nuclear Rocket Program Quarterly Progress Report Fourth Quarter 1965 ANL-7150 Eric PROUST Lecture Series on SPACE NUCLEAR POWER & PROPULSION SYSTEMS -2-Nuclear Thermal Propulsion Systems (last updated in January 2021)

u 1956 -1972, Project ''Rover'' / NERVA (Nuclear Engine for Rocket Vehicle Application) Source: Nuclear Thermal Propulsion Ground Test History -The Rover/NERVA Program; Harold P. Gerrish, NASA Marshall Space Flight Center, February 25, 2014

Source: The Nuclear Thermal Propulsion Stage (NTPS): A Key Space Asset for Human Exploration and Commercial Missions to the Moon, Stanley K. Borowski et al., , NASA/TM-2014-218105, AIAA-2013-5465

Source: Clayton W. Watson, Nuclear Rockets: High-Performance Propulsion for Mars, Report LA-12784-MS, UC-743, May 1994

Source: George Schmidt, Nuclear Systems for Space Propulsion and Power, FISO Seminar, 8 Dec. 2010

the most powerful nuclear rocket reactor ever tested (1968, Rover/NERVA stand and during a high-power test The reactor operated for ~32 minutes, Achievements of the Rover/NERVA Program 20 * See back-up slide $ 1 kg 235U ~ 2 10 -3 Ci ORION Mars by 1965, Saturn by 1970)

Until 2010 Obama's Space Policy Directive: By the mid-2030s, send humans to orbit Mars The Open-Cycle Gas-Core Reactor Concept (US, mid 60's to early 70's)

56

Ball of Uranium plasma (peak/edge temperature ~55 000/ 26 000 K) hydrodynamically confined (vortex stabilized) by the flowing hydrogen propellant (which enters the cavity by flowing though its porous graphite wall)

Hydrodynamically confined Uranium plasma occupies no more than ~25% of the cavity to limit the amount of uranium lost (scrapped off by and exhausted with the hydrogen propellant) to 1/100 to 1/400 of the propellant flow High Hydrogen Pressure (~500 atm) to ensure criticality / an appropriate Uranium density (~6 10 -3 g/cm 3 ) in the plasma within a cavity of reasonable diameter (~4 m); critical mass ~ 50 kg U(98% U5) with ~60 cm thick BeO moderator/reflector (high temperature hydrogen has a negative impact on reactivity due to upscattering); Engine for 196 kN at 4400 s Isp: 6000 MWth, ~258 tons (60% due to radiator!)

Hydrogen propellant seeded with ~5w% C or W nanoparticles absorbs >99% of the heat radiated by the Uranium plasma; Isp up to 6500 s ~7% of the power (gammas and neutrons) deposited in the cavity wall and moderator/reflector; their regenerative cooling by H 2 possible up to Isp < 3000 s; beyond, need for a cooling circuit & radiator to dissipate heat.

Radiator dominates engine mass at high thrust (at > ~110 kN for 5000 s Isp) In the US, a mostly empirical experimental program on:

-Plasma stability, -Uranium plasma emissivity, -Hydrogen opacity, -Gas-phase criticality (3 zero power reactors built)

Lack of computational capabilities and plasma dynamics in its infancy at that time, accurate assessment of the chaotic, complex behavior of a fluid-stabilized plasmoid was unreachable Concept revisited in the late 90's, challenge of hydrodynamic confinement confirmed ð innovative configurations?

Open-Cycle Gas-Core Nuclear Thermal Propulsion (US & USSR, 60's to early 70's)

In the USSR: hydrodynamic + magnetic confinement The jet power is ~8% of the power released by D-T fusion, and 15 times the laser driver power

The spacecraft surface is a huge radiator (radiating 14 GW at 1 500 K requires 62 000 m 2 ) An inductor power system extract ~5% of the debris energy to power the laser drivers A "Nuclear Safe Orbit": a (typically 1000 km or higher) orbit providing an unattended orbital life of sufficient lifetime (typically 10 000 y or more) so that the core's radioactive nuclide inventory will have decayed down to ''acceptable'' levels Typical 300 kWe SNPS core activity decay An NTR needs to come to full power very quickly after the onset of hydrogen flow, or the wasted hydrogen will significantly reduce the Isp of the system Chill-down of the various engine components takes ~60 s.

The engine can then be turned on to full power et a rate limited by thermal stresses in the core resulting from the transient.

For NERVA-type engines, the rate of core temperature raise was not to exceed 83 K/s.


Dr Christian Laugier

Bayesian & AI based Perception and Decision-making for Autonomous Navigation in Complex Dynamic, Uncertain and Human populated Environments.

Each research period has conducted to published scientific results, to software developments and to some innovative achievements that have been transferred to the industry, or that have led to the creation of start-ups. A brief overview of this research activity & main achievements for the periods 1 to 4 is available on my home page http:

Achievements 1: A new research framework for "Robots in Human Environments"

As previously mentioned, I have defined and launched in 2004 a new research line for addressing scientific problems related to the future developments of robots operating in Highly Dynamic and Human-populated Environments. This has led to the development of a new framework for addressing Perception and Decision-making issues, under strong Uncertainty, Dynamics, and Human interaction constraints.

The main impact of this new research framework can be summarized as follows: o This new approach has led to original well published and referenced scientific results in the IEEE communities of Robotics & Automation (RAS) and of Intelligent Transportation Systems (ITSS), and to several patents and valorization actions. See next sections for more details.

o This new scientific positioning and results have permitted us to obtain National (ANR, IRT Nanoelec, FUI, etc.) and European (IP BACS, IP Prevent, ECSEL Enable-S3, ECSEL CPS4EU, etc.) projects, but also to sign important bilateral and multiannual industrial contracts (Toyota Europe, Denso Japan, Renault, EasyMile, etc.).

o Thanks to this early positioning and interesting results obtained on this emerging research line, our research team has obtained an international recognition has witnessed by the following events, actions and awards (see achievements 4 & 5 for more details):

-IEEE RAS Technical Committee (TC) "AGV-ITS" 4 created by C. Laugier in 2004 with the objective to develop, to structure and to lead the RAS scientific community in the emergent field of "mobile robots and autonomous vehicles". This TC has been renewed several times after having obtained positives evaluations by the IEEE RAS ad hoc committee. The chairs of this TC have also received -Numerous invitations for Keynote or Plenary Talks in some major conferences or scientific events of the IEEE RAS and ITS communities (see list of publications). -Intensification of the long-term cooperation (R&D contracts, PhD theses, Patents, Software licenses) with some strategic industrial partners: Toyota (since 2005) and with Renault (since 2010).

Achievements 2: The "Embedded Bayesian Perception" paradigm

Robust perception in Open and Dynamic Environments populated by Human beings is an open and challenging scientific problem. Traditional perception techniques can hardly been applied for solving such a problem, mainly because such environments are uncontrolled and generate new strong constraints to be satisfied (in particular high dynamicity and uncertainty). This means that the proposed solutions have to simultaneously take into account various characteristics such as real-time processing, sensors limitations, continuous dynamic world changes, or near future expected evolution of the robot environments (concept of "prediction"); these solutions have also to include explicit models for reasoning about uncertainty (data incompleteness, perception errors, hazards of the physical world).

For that purpose, we have proposed a new paradigm called "Bayesian Perception". The foundations of this approach relies in the concept "Bayesian Occupancy Filter (BOF)" [1,2,4] initially developed in our research team in the PhD thesis of a Christoph Coué (2004), and more recently improved toward an embedded version in the PhD thesis of my former student Tiana Rakotovao (2017) [START_REF]Intelligent Vehicle Perception: Toward the Integration on Embedded Many-core[END_REF][START_REF]Integration of Multi-sensor Occupancy Grids into Automotive ECUs[END_REF]. The basic idea is to combine à Bayesian filter with a probabilistic grid representation of the both space and the moving entities. This model allows the filtering and the fusion of heterogeneous and uncertain sensors information coming from a dynamic space; this Bayesian filtering takes into account the history of the sensors measurements, a probabilistic model of the sensors and of the uncertainty, and a dynamic model of the observed objects motions. An interesting property of this approach is to continuously generate conservative predictions about the most probably future evolution of the environment (i.e. when velocities are supposed to be stable during a small amount of time), even if the sensing information is temporary not available.

launched a new partnership with the CEA 5 Grenoble and ST Microelectronic, with the objective to redesign the approach for developing a software / hardware integration based on a multi-core architecture. Such an integration is clearly necessary for a future deployment of the technology (for obtaining a drastic reduction of the size, weight, energy consumption, and cost, while improving the efficiency of the process). This new partnership with the CEA Grenoble has conducted to the launch of a 10-year project in the scope of the IRT

As this research topic has already reached a good level of maturity, we have recently started to bend our research work for the next 5 years with the aim to represent, extract and process semantic information (in particular by integrating Computer Vision and Deep Learning approaches). See Part 2 of this report.

Achievements 3: Situation Awareness, Prediction & Risk Assessment, Decision under Uncertainty

Robust perception (using multiple sensors) is the first step towards the analysis and the understanding of a complex dynamic scene, which in turn is a prerequisite for the planning, navigation decision and control steps. Since the environment is dynamic and uncertain, both a consistent estimation of the current state and a prediction of the most likely evolution of the scene and of its associated risk of future collisions, are necessary for being able to take safe and socially acceptable navigation decisions. As far as we know, there was few significant scientific contributions on this subject before the second decade of the 21 th century (see our journal survey paper [10]). The IEEE RAS Technical committee AGV-ITS 3 annually organizes scientific events focusing on advanced technologies for Intelligent Vehicles; these events are attracting more and more scientists (around 300 participants in the last two years).

Scientific Awards obtained in this recent research line:

-Best European PhD thesis Award to my former PhD student D. Vasquez (2010) o Contribution to Technological transfers:

-Several technological transfers have been carried out with our former startups in the fields of Robotics, CAD-Robotics, and Dynamic simulation with Human interaction. -Our "Bayesian Occupancy Filter" technology has been transferred to Probayes SA in 2006 (for commercialization within the ProBT Bayesian Library). -Our "Bayesian Perception System software CMCDOT" has been transferred to Easymile and to Toyota in 2019.

o Patents (more details in section "Publications"):

-"Automatic Parallel Parking for cars" (1996) 17 -Patent application cancelled because of prior partial disclosure.

-"Bayesian Occupancy Filter -Initial concept" (2005) -Patent hold by Inria.

-"Bayesian Occupancy Filter -Improved industrial version" (2005) 18 Figure 1 gives a very broad overview of our Embedded Perception & Decision-making approach (more details are available in the past activity reports of the team and in our publications, e.g. [4,7,[START_REF] Tay | Probabilistic Vehicle Motion Modeling and Risk Estimation[END_REF][START_REF]Risk Assessment at Road Intersections: Comparing Intention and Expectation[END_REF][START_REF]Relever les défis des véhicules autonomies[END_REF]. Another important topic that we have recently started to address is the challenging problem of "Validation & Verification" (see section 3). The following sections briefly present the avenues of research proposed to address the above questions. To conduct this Research and Innovation program, we have established over the last decade some targeted long-term partnerships:

-A 10 years Strategic partnership and R&D program with CEA 5 & IRT 2 Nanoelec (launched in 2011), for addressing the Software / Hardware integration issues for our "Bayesian Embedded Perception" approach. The related SVA (Safety of Autonomous Vehicles) R&D program involves several industrial companies such as ST-Microelectronics or Schneider Electric, and several SMIs such as Akeoplus or EasyMile. Through this strategic partnership, we have carried out several R&D projects having given rise to a PhD thesis, several well-published results, three shared patents and a technological transfer. In order to validate our models & algorithms, we have developed a fairly complete experimental platform including an Automated Renault Zoe equipped with multiple sensors, V2X communication facilities, and our on-board Bayesian Perception / Decision-making / Control systems. As part of a cooperation with Schneider Electric, we recently addressed the concept of "green micro-mobility" in cities, with the objective of incubating a start-up to market the resulting product (a fleet of selfservice electric light autonomous vehicles); the main challenge is to adapt our Bayesian perception / decision-making / control approaches to the use of inexpensive (but less efficient) sensors, while being able to operate in real-time with limited computational capacities.

-Long-term R&D agreement with Toyota (since 2005): The Inria-Toyota agreement was renewed respectively in 2014 and in 2018 for additional periods of 4 years. The main research topics covered address the problem of autonomous driving, and more specifically the analysis of road scenes, the prediction and assessment of collision risks, and the use of AI learning techniques for the interpretation of complex road scenes and for the modeling of driver's behaviors. This research work has given rise to numerous publications, 2 PhD theses and to several shared patents.

-

Long-term research agreement with Renault (since 2009):

The Inria-Renault agreement (mainly for PhD theses) has been renewed several times for periods of 3 to 4 years (the current agreement covers the period 2019-21). The main research themes covered address the problem of driving assistance (ADAS) and of autonomous driving (AD), with a focus on the safety issue at road intersections (2 PhD theses defended) and on dangerous road situations (PhD thesis to be defended by the end of 2021). This research work has given rise to numerous publications, 2 PhD theses and to several shared patents.

-Recent collaboration with EasyMile (since 2018): A 2 years collaborative project with EasyMile was launched in 2018 in the scope of IRT Nanoelec. The objective was to adapt our Bayesian Perception approach to the context of autonomous shuttles (the obtained results are confidential). We are still cooperating with EasyMile in the scope of several R&D projects with Renault (FUI Tornado project) and with Iveco (FUI STAR project).

- -PhD students: Luiz Serafim-Guardini (PhD thesis Inria-Renault, 3 rd year), Manuel Diaz-Zapata (PhD thesis, 1 st year) -Non-permanent Inria Engineers: J. Lussereau, T.Genevois, R. Asghar, A. Paigwar, A. Suresh, A. Gonzalez-Moreno.

Bayesian & AI driven Embedded Perception o Brief problem statement & Background

Perception is known to be one of the main bottlenecks for robot motion autonomy, in particular when navigating in open and dynamic environments is subject to strong real-time and uncertainty constraints. In order to overcome this difficulty, we proposed some years ago a novel paradigm in Robotics called "Bayesian Perception"

21

. The basic idea is to combine a Bayesian filter with a probabilistic grid representation of both the space and the velocities of the moving entities. It allows the filtering and the fusion of heterogeneous and uncertain sensors data, by taking into account the history of the sensors measurements, a probabilistic model of both the sensors and the uncertainty, and a dynamic model of the observed objects motions. The core technology is our concept of "Bayesian Occupancy Filter" (for constructing "Dynamic Occupancy Grid"), Figures 2 illustrates [PhD thesis C. Coue, 2004] [1,2,4,7,[START_REF]Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering[END_REF][START_REF]Tutorial on Autonomous Vehicle Technologies for Perception & Decision-making[END_REF]. During the period 2014-20, the concept of "Bayesian Perception" has further been developed, expended and valorized. The new version of our Bayesian Perception system is called CMCDOT

22

. This research work has given rise to a numerous international publications [START_REF] Negre | Hybrid Sampling Bayesian Occupancy Filter[END_REF][START_REF]Probabilistic Grid-based Collision Risk Prediction for Driving Application[END_REF][START_REF]Conditional Monte Carlo Dense Occupancy Filter[END_REF][START_REF]XDvision: Dense outdoor perception for autonomous vehicles[END_REF][START_REF]Intelligent Vehicle Perception: Toward the Integration on Embedded Many-core[END_REF][START_REF]Integration of Multi-sensor Occupancy Grids into Automotive ECUs[END_REF][START_REF]Bayesian Perception and Decision: From Theory to Real World Applications[END_REF][START_REF]Towards Fully Autonomous Driving ? The Perception & Decision-Making Bottleneck[END_REF][START_REF]Dynamic Traffic Scene Understanding using Bayesian Sensor Fusion and Motion Prediction[END_REF][START_REF]A Journey in the history of Automated Driving[END_REF], the PhD defense of Tiana Rakotovao in 2017 (New models for embedded version on ECU, thesis co-supervised with CEA LETI), the filing of several patents, the sale of software licenses to industrial companies, and an ongoing technology transfer with a SME marketing autonomous shuttles, Figure 3

illustrates (process & results not published because of industrial confidentiality).

o Overview of the R&D agenda and results

As this research line has already reached a good level of maturity, we are going to bend our research work for the next 5 years with the aim to represent, extract and process semantic information (in particular by integrating Computer Vision and Deep Learning approaches). Therefore, the main research themes to be emphasized in the coming years are listed below:

-New mathematical formulation & implementation of the CMCDOT system. The main objective is to improve the efficiency and the robustness of the perception system in complex and challenging dynamic situations, while drastically reducing the number of false positives and negatives. New mathematical models & algorithms have recently been developed, implemented and tested. The improvements over to the current version of the CMCDOT are significant. Current work focuses on a better integration of semantic information in this process, e.g. free space, unknown space, characteristics of static or dynamic obstacle, motion information, clusters of cells having similar properties, motion prediction, collision risk assessment, etc.). A new patent (called CMCDOT+) based on some of these improvements has recently been filed.

-New concept of "Semantic Occupancy Grid" & Related AI-based approaches. The objective is to integrate semantic information in the perception process, in order to have a better understanding of the observed dynamic scenes. To this end, we started 3 years ago to develop models & algorithms to extract, represent and process semantic information within the framework of our Bayesian Occupancy Grid formalism. The objective is to build "Semantic Occupancy Grids" by fusing CMCDOT representations with semantically segmented RGB images (road, sidewalk, buildings, cars, pedestrians, etc.). Our approach relies on the use of Deep Learning methods (i.e. using pre-trained high capacity Deep Neural Networks), both to perform the semantic segmentation of RGB images and to merge the CMCDOT occupancy grids with semantically segmented RGB images, Figure 4 illustrates [START_REF] Sierra-Gonzalez | Modeling Driver Behavior from Demonstrations in Dynamic Environments Using Spatiotemporal Lattices[END_REF][START_REF] Erkent | Semantic Grid Estimation with a Hybrid Bayesian and Deep Neural Network Approach[END_REF][START_REF] Erkent | End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids[END_REF].

In parallel, we have started to develop additional methods to perform: (1) the semantic classification of 3D point-clouds (e.g. ground, obstacles, unknown, etc.) [START_REF]Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field[END_REF]54], and (2) the detection & classification of 3D objects in point-clouds (with or without merging with RGB images) [START_REF]Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds[END_REF][START_REF] Gomez-Hernandez | Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy[END_REF], Figure 5 illustrates.

-

AI-based approaches to cope with Varying Weather Conditions and to reason at the Object level. (i) Unsupervised Domain Adaptation:

Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions; however, varying weather conditions reduce the accuracy of the semantic segmentation of RGB images. Deep Neural Networks (DNNs) proved to have a high accuracy and fast implementation in semantic segmentation. However, a tremendous amount of labeled data would be necessary for each weather condition with supervised learning; therefore, we perform Unsupervised Domain Adaptation (UDA) so that labeled images are not required for adaptation. The old weather condition is called "source domain" in UDA, while the new weather condition is called "target domain". Figure 6 illustrates [START_REF] Erkent | Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles[END_REF].

(ii) Reasoning at the object level: Understanding a complex dynamic scene also requires to reason at the object level (i.e. in terms of space occupation, dynamic properties and behavioral models). This is why we have also started to address additional segmentation methods such as "Instance Segmentation" or "Panoptic segmentation" 23 [START_REF] Zapata | Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles[END_REF]. These methods are still under development as part of Manuel Diaz-Zapata's PhD thesis. The next step is to use this rich semantic information to drastically improve existing DATMO 24 approaches, i.e. by inserting a new "classification" step. This R&D work is carried out as part of our long-term cooperation with Toyota and of the EU project CPS4EU. 23 "Semantic segmentation's" goal is to label an image at the pixel level (grass, sky …) -"Instance segmentation" focuses on countable objects (people, cars ..) by delimiting them in the image using bounding boxes or segmentation masks -"Panoptic segmentation" combines segmentation and recognition tasks. 24 DATMO: Detection and Tracking of Multiple Objects. [START_REF] Erkent | Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles[END_REF] for more details.

AI-based Situation Awareness & Decision-making for Autonomous Navigation o Brief problem statement & Background (i) Short-term Motion Prediction & Collision Risk assessment:

Predicting the evolution of moving agents perceived in a dynamic and uncertain environment is essential to be able to navigate safely in such an environment (i.e. making the most appropriate navigation decisions in real-time). We recently showed that an interesting property of the Bayesian Perception approach is that it generates conservative27 short-term predictions about the likely future evolution of the observed scene, even if the sensing information is temporarily incomplete or not available. See our publications about the CMCDOT & Collision Risk Assessment approaches [12, 15, 16, 17, 18] [20, 23, 24, 25], Figure 7 illustrates. [START_REF] Negre | Hybrid Sampling Bayesian Occupancy Filter[END_REF][START_REF]Conditional Monte Carlo Dense Occupancy Filter[END_REF]. The approach combines our "Conservative short-term Motion Prediction model" (horizon 3-5 seconds), with a probabilistic estimation of the Collision Risks.

(ii) Semantic-based Motion Prediction & Situation Awareness:

In environments populated by humans, the estimation of more abstract properties (e.g. object classes, affordances, agent's intentions and behaviors, etc.) becomes necessary in order to better understand the future 28 evolution of the scene, to be able to predict potential collisions, and therefore to be able to make the most appropriate navigation decisions. This is why, the main objective of this new line of research is to design an AI-based framework to address the key problem of "Situation Awareness & Decision-making for Autonomous Driving", taking into account the aforementioned features, while overcoming the shortcomings of traditional rules-based reasoning approaches. Indeed, these traditional approaches often work well in low complexity situations, but they also lead to a lack of scalability and mid/long-term forecasting capabilities.

Our approach to solve this problem is to generalize and combine our previously published and patented contributions (obtained through our long-term collaborations with Toyota and Renault respectively), while emphasizing the links between machine learning, motion planning, and decision-making using Dynamic Bayesian Networks (DBN) and / or Partially Observable Markov Decision Processes (POMDP). Our previously developed approach for behavior-based collision risk assessment is illustrated in Figure 8. The improvements expected from the introduction of new AI-based methods are discussed in the following sections. Right: Collision risk assessment using our "Intention & Expectation" paradigm, implemented using a Dynamic Bayesian Network. This approach is inspired by human reasoning and is has been designed to better take into account the interactions between vehicles in a mixed traffic context (i.e. when some human drivers are in the loop). Best student paper IEEE IV 2012 [START_REF]Risk Assessment at Road Intersections: Comparing Intention and Expectation[END_REF], [Patent n° 5 with Renault], [START_REF]Impact of AI on Autonomous Driving[END_REF].

(iii) Validation & Certification: Autonomous vehicles are in practice Complex & Critical Cyber Physical Systems (CPS), designed to operate under various real-world conditions (high dynamicity, uncertainty, unexpected events, etc.) and to interact with human beings. A few year ago, we started to tackle the open question of the "Validation & Certification" of such systems. Indeed, solving this problem is of paramount importance, in order to allow a future deployment of such systems in the daily living space of people. The main idea of our approach is to combine Realistic Simulation techniques with Formal methods. Some preliminary results have recently been obtained and published by our team within the framework of the EU Enable-S3 project [START_REF] Quilbeuf | Statistical Model Checking Applied on Perception and Decision-making Systems for Autonomous Driving[END_REF][START_REF] Barbier | Validation Framework Applied to the Decision-Making Process for an Autonomous Vehicle Crossing Road Intersections[END_REF]52], Figure 9 & 10 illustrates.

o Overview of the R&D agenda and results

The main objective for the next few years is to extend our current approaches for "dynamic scene understanding & decision making for autonomous driving" [30, 31, 37, 45, 64] [32, 40, 46], in order to be able to cope with realistic mixed traffic scenarios. The focus is now on the development of models and algorithms to exploit the mutual benefits of Bayesian and AI approaches, in order to be able to cope with complex mixed traffic scenarios requiring the processing of large data sets and associated semantic information. Emphasis will be placed on the knowledge and learning components of these approaches. In addition, we will continue to develop the validation & certification methods that we have started to address in recent years (by combining realistic simulation and formal methods). Therefore, the main research themes to be highlighted in the coming years are listed below:

-Prediction & Semantic Characterization of Collision Risks. The main objective of this line of research is to integrate semantic information into the heart of our models for Motion Prediction & Collision Risk assessment. Emphasis is placed on the integration of knowledge learned on the semantics of the traffic environment, as well as on behavioral & interaction models of road users. The main objective is on the one hand to drastically improve the quality of the predicted motions for all the surrounding traffic participants, and on the other hand to semantically characterize 29 the risks of collision between the ego vehicle and the other traffic participants [START_REF]Impact of AI on Autonomous Driving[END_REF][START_REF]A Journey in the history of Automated Driving[END_REF]. Then, this contextual information can then be leveraged to select the most appropriate and safest navigation or collision avoidance strategy. This research work is closely linked to the concept of "semantic grid" developed within the Bayesian Perception research line presented in the previous section. It also allows us to consider additional criteria such as the "severity of injuries" in collision mitigation scenarios, Figures 11 & 12 13 illustrates the approach we are developing for highway environments (in cooperation with Toyota) [START_REF]High-Speed Highway Scene Prediction Based on Driver Models learned from Demonstrations[END_REF][START_REF]Interaction-Aware Tracking and Lane Change Detection in Highway Scenarios Using Realistic Driver Models[END_REF][START_REF] Sierra-Gonzalez | Modeling Driver Behavior from Demonstrations in Dynamic Environments Using Spatiotemporal Lattices[END_REF][START_REF] Gonzalez | Human-Like Decision-Making for Automated Driving in Highways[END_REF].

Our current research work focuses on Autonomous Driving in mixed traffic mode30 , while considering various traffic environments and their associated models adaptations (e.g. highways, country roads, urban streets, road intersections, etc.). The main objective is to develop a new framework for "Human-like decision-making for Automated Driving", having the ability to be easily adaptable to a wide class of traffic scenarios (e.g. highways, rural roads or urban environments), as well as different driving modes (full autonomy, driving assistance, mixed traffic mode). By "human-like", we refer to driving actions that respect the traffic rules, show anticipative behavior, avoid collisions (when possible), safely overtake some other vehicles in order to optimize travel time, etc. Another important feature of this approach is to make autonomous driving decisions more understandable to other nearby human drivers, and thus reduce the likelihood of collision between an autonomous vehicle and another human driven vehicle.

Figure 13: Illustration of our approach for Autonomous Driving in highways [PhD thesis David Sierra- Gonzalez,[START_REF]High-Speed Highway Scene Prediction Based on Driver Models learned from Demonstrations[END_REF][START_REF]Interaction-Aware Tracking and Lane Change Detection in Highway Scenarios Using Realistic Driver Models[END_REF][START_REF] Sierra-Gonzalez | Modeling Driver Behavior from Demonstrations in Dynamic Environments Using Spatiotemporal Lattices[END_REF][START_REF] Gonzalez | Human-Like Decision-Making for Automated Driving in Highways[END_REF][START_REF]A Journey in the history of Automated Driving[END_REF].

Left: Learn driver behaviors from real driving demonstrations (dataset built using our Lexus vehicle) and Inverse Reinforcement Learning. Right: A realistic human-like driver model is used to Predict the long-term evolution (10s and beyond) of traffic scenes in highways. For short/mid-term, both the "Driver model" and the "Dynamics of the target" provide useful information to determine future driving behaviors. Our probabilistic model fuses Model-based Predictions & Dynamic evidence to produce robust estimate of lane change intentions.

Indeed, sharing the road with humans constitutes, along with the need for robust perception systems, one of the major challenges slowing the large-scale deployment of automated driving technology. The actions taken by human drivers are determined by a complex set of interdependent factors, which are very hard to model (e.g. intentions, perception, emotions). Therefore, any prediction of human behavior will always be inherently uncertain, and will become even more so as the prediction horizon increases. Then, fully automated vehicles must make navigation decisions based on uncertain states and intentions of surrounding vehicles. This research work is mainly carried out in the framework of our long-term cooperation with Toyota and of the CPS4EU EU project.

- systems. This work was initially carried out as part of the Enable-S3 EU project, in cooperation with Renault and with the Tamis (Inria Rennes) and Convecs (Inria Grenoble-Alpes) teams working in the field of Formal Methods. The basic idea is to apply a "cosimulation approach" consisting in combining (1) real experiments with an autonomous vehicle, (2) numerous realistic simulations of the same scenario by applying random changes in the values of some parameters, and (3) an analysis of the "execution traces" using a formal method (e.g. SMC: Statistical Model Checking) and some selected "Key Performance Indicators (KPIs)" [52]. Current and future work will focus on both the realistic co-simulation and formal verification components. It will mainly be carried out over the next 4 years as part of both the IRT Nanoelec SVA project and the French R&D Prissma project (involving academic research teams and manufacturers from the automotive sector).

-Experimental validation & platforms

Real world experiments will continue to be carried out using the Inria & IRT Nanoelec experimental plateforms and several protected test tracks (Figure 14 illustrates). For obvious safety reasons, experiments in real traffic conditions (highways, rural roads, urban areas, etc.) are carried out under the control of an experienced human driver.

Publications

A more complete list of publications is available in my extended CV: hal-03147561

Significant publications related to the new research framework (period 2006-13)

  two IEEE RAS TC Awards in 2006 and 2013 respectively. -Increasing success of the organization during the period 2007-20 of 12 workshops on "Planning, Perception and Navigation for Intelligent Vehicles" (PPNIV series) in the major conferences of the domain (IEEE/RSJ IROS, IEEE ICRA and IEEE IV). These workshops attracted an increasing number of attendees (50 attendees at ICRA 2007, 130 attendees at IROS 2013 and a record number of 350 attendees at IROS 2018 and 2019). -Strong involvement in the organization of several IEEE major conferences (ICRA, IROS, IV), and to scientific events (workshops, special sessions, forums) organized in the scope of these conferences. -Participation as Invited Associate Editor & Author to the publication of two Springer Handbooks: Handbook of Intelligent Vehicles (2012) and 2 nd edition of the Handbook of Robotics (2014). -Member of the Editorial Board and/or Steering Committee of several IEEE journals (e.g. IEEE T-ITS, IEEE T-IV, IEEE Robomech). -Several Scientific Awards related to the research results obtained in this new framework.

o

  -Patent hold by Inria & Probayes SA. -"Continuous collision risk assessment for improving vehicle safety" (2010) -Patent hold by Inria, Toyota, Probayes. -"Collision risk assessment at roads intersections" (2012) -Patent hold by Inria & Renault. -"Methods and apparatus for improving driving safety" (2013) -Patent hold by Inria. -"Decision making for collision avoidance systems" (2013) -Patent hold by Inria & UC Berkeley. -"Dynamic scene analysis method and associated analysis module and computer programme" (2015) -Patent hold by Inria & CNRS & CEA / IRT Nanoelec. -"Dispositif informatique pour l'aide à la conduite" (2017) -Patent hold by Inria & CEA / IRT Nanoelec. -"Driving Assistance Method and System" (2017) -Patent hold by Inria & Toyota. -"Semantic Occupancy Grid Estimation" (2018) -Patent hold by Inria & Toyota. -"Computerized device for driving assistance" (2020) -Patent under application by Inria & CEA / IRT Nanoelec. o Bilateral industrial partnerships: The abovementioned technologies have permitted us to establish several long-term bilateral industrial partnerships: -ProbaYes (since 2004): Commercialization of the Bayesian Library ProBT & Collaboration on R&D projects with Toyota. -Toyota (since 2005): Long-term R&D collaboration, 2 PhD grants, several common patents, common experimental platform (Equipped Lexus). -Denso (2005-08): Several R&D contracts for the evaluation and the adaptation of the "Bayesian Occupancy Filter" technology to the Denso Automotive Lidar. The license & royalties agreement negotiated during the year 2008 was finally not been signed. -Renault (since 2010): Long-term R&D collaboration, several contracts, 2 PhD grants, several common patents, transfer of the developed technology on the Renault experimental platform (Renault Technocentre, R&D purpose). -EasyMile (since 2018): R&D project "Embedded Bayesian Perception for autonomous mobility", in the scope of the IRT Nanolelec SVA project and of several other national R&D projects. -Schneider (2018-19): R&D project in the scope of IRT Nanoelec, for the development of a "light autonomous electric vehicle for micro-mobility". -Industrial scientific expertise for "future mobility systems": Scientific Advisor for Baidu ApolloScape project on autonomous vehicles (2018-19); Scientific expertise for Renault-Nissan of an industrial dossier on the concept of "Robot Taxi" (2017).-Part 2 -Outline of the Scientific Project & Research AgendaResearch Project: Bayesian & AI driven Embedded Perception and Decision-making. Application to Autonomous Navigation in Complex, Dynamic, Uncertain and Human-populated Environments1. Outline of the Scientific Project and R&D ContextThe scientific project to be tackled over the next 5 years can be seen as a more in-depth exploration of the scientific theme initially launched within the framework of the Inria project-ream eMotion, then studied in more depth over the period 2015-20withinthe Inria project-team Chroma. This thematic focuses on the promising paradigm of "Bayesian & AI driven Perception and Decision-making for Autonomous Navigation in Complex, Dynamic, Uncertain and Human-populated Environments". The scientific foundations of this research line, as well as the main achievements during the period 2004-20 have been briefly described in the first part of this report. Brief problem statement & approach Robust perception & Decision-making for safe navigation in open and dynamic environments populated by human beings is an open and challenging scientific problem. Traditional approaches do not provide adequate solutions for these problems, mainly because such environments are partially unknown, open and subject to strong constraints to be satisfied (in particular high dynamicity and uncertainty). This means that the proposed solutions have to take simultaneously into account characteristics such as real-time processing, temporary occultation or false detections, dynamic changes in the scene, prediction of the future dynamic behaviors of the surrounding moving entities, continuous assessment of the collision risk, or decision-making for safe navigation.

Figure 1 :o

 1 Figure 1: Illustration of our approach for addressing the problem of Embedded Perception & Decision-making for Autonomous Vehicles (by mixing Bayesian & AI approaches)

Figure 2 :

 2 Figure 2: The Bayesian Perception approach. Top: Bayesian Occupancy Filter paradigm. Middle: Bayesian Filtering Process & Additional features. Bottom: Application to the continuous characterization of the safe navigable space in front of the ego vehicle.

Figure 3 :

 3 Figure 3: Integration of the CMCDOT system on a commercial autonomous shuttle (2019). Three main steps: (1) Implemented on Nvidia TX1, (2) Connected to the shuttle control system using ROS, (3) Successfully tested in real-time (at low speed) with the onboard sensors of the shuttle.

Figure 4 :

 4 Figure 4: Constructing "Semantic Occupancy Grid" by fusing CMCDOT Occupancy Grids with semantically segmented RGB images. Left: Overview of the method: i: RGB Image, l: LIDAR data, o: Occupancy grids, s: Output of the segmentation network, p: registered planes as inner representations, g: semantic grid. The continuous black arrows represent the path on which the parameters are learned jointly end-toend including the registration process, while the blue dotted arrows show the process of the occupancy grid computation.Right: Three scenes with RGB images, Ground Truth (GT), Semantic segmentation predictions (Prediction) and Results of the CRF refinement. Scene 1 and 2 are from Kitti dataset, whereas scene 3 is from our own experimental dataset[START_REF] Erkent | End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids[END_REF].

Figure 5 :

 5 Figure 5: 3D object detection in point clouds leveraging dynamic occupancy grid representations of the environment[START_REF]Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds[END_REF]

Figure 6 : 25 .

 625 Figure 6: Illustration of our Deep Neural Network (DNN) approach for Unsupervised Domain Adaptation. Top Left: Adaptation training performed using random RGB images from both "Source domain" (old weather conditions) and "Target domain" (new weather conditions), by using sample images from Foggy Cityscapes Dataset 25 . The semantic segmentation model has the architecture of MobileNetV2 26 . Top Right: Adaptation with self-supervision of features. Green dotted boxes represent the segmentation model. Dotted black lines represent the back-propagation. Blue lines represent the process without back-propagation. Bottom: Berkley Driving Dataset sample -Left: RGB in rainy; Center: No adaptation (inappropriate segmentation); Right: DLV3 MMD rainy adaptation (road, landscape, 3 cars correctly classified). See[START_REF] Erkent | Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles[END_REF] for more details.

Figure 7 :

 7 Figure7: Illustration of the concept of "Bayesian Short-term Collision Risk" implemented in the CMCDOT system[START_REF] Negre | Hybrid Sampling Bayesian Occupancy Filter[END_REF][START_REF]Conditional Monte Carlo Dense Occupancy Filter[END_REF]. The approach combines our "Conservative short-term Motion Prediction model" (horizon 3-5 seconds), with a probabilistic estimation of the Collision Risks.

Figure 8 :

 8 Figure 8: Mid-term collision risk assessment by reasoning on vehicle's Behaviors & Interactions. Left: Trajectory prediction using objects perception & learned behaviors + Collision risk assessment using Monte-Carlo simulation. Patented by Inria, Toyota and Probayes [12] [Patent n° 4 with Toyota].Right: Collision risk assessment using our "Intention & Expectation" paradigm, implemented using a Dynamic Bayesian Network. This approach is inspired by human reasoning and is has been designed to better take into account the interactions between vehicles in a mixed traffic context (i.e. when some human drivers are in the loop). Best student paper IEEE IV 2012[START_REF]Risk Assessment at Road Intersections: Comparing Intention and Expectation[END_REF], [Patent n° 5 with Renault],[START_REF]Impact of AI on Autonomous Driving[END_REF].

Figure 9 :

 9 Figure 9: Validation testing using simulated scenarios. Left: Simulated scenarios in CARLA with the ego-vehicle (white) colliding or not with another vehicle (red) or with a pedestrian crossing the road. Right: Simulated Lidar sensor and corresponding dense occupancy grid generated by the CMCDOT perception system.

Figure 10 :

 10 Figure 10: Illustration of one of the experiments performed with Inria's autonomous car. The scenario reproduces the car colliding with a pedestrian (mannequin) crossing the street [52]. Top: Camera view from the car. Bottom: The environment as seen by the Lidar and the CMCDOT "Collision risk grid". The sequence from left to right represents different time stamps. Colors represent the probability of collision in 3s (red), 2s (yellow), 1s (white) and 0s (crash).

  illustrates[START_REF] Serafim Guardini | Employing Severity of Injury to Contextualize Complex Risk Mitigation Scenarios[END_REF]. Current work focuses on on-line Planning & Selection of the most appropriate collision avoidance or mitigation trajectories, using an adapted MPPI (Model Predictive Path Integral) approach. This research work is mainly carried out in the framework of the IRT Nanoelec SVA project, of the PhD thesis of Luiz Serafim-Guardini (in cooperation with Renault) and of the ES3CAP project.

Figure 11 :

 11 Figure 11: Illustration of injury curves. Left: Example of scenario. Middle: Vulnerable Road Users (VRU) perspective (safety of vulnerable users takes priority over vehicles). Right: Political/economical point of view (the safety of the occupants of the ego vehicle is a priority).

Figure 12 :

 12 Figure 12: Probability of Collision with Injury Risk (PCIR) approach, evaluated using KITTI dataset -Top: Scenario analyzed with a classification of moving entities -Bottom left: Results from the VRU perspective -Bottom right: Results for the Economical/political perspective.-Human-Like Decision-making for Automated Driving. Over the past few years, we have started to developed a new IA-based approaches to address the problems of Automated Driving in highways (at high speed) and in Urban road Intersections (at low speed). This research work has given rise to several international publications[30, 31, 37, 45, 64] [29, 32, 40, 46], patents [Patent n° 7 with UC Berkeley] [Patent n° 10 with Toyota] and to the PhD theses of David Sierra-Gonzalez and of Mathieu Barbier (respectively in cooperation with Toyota and Renault). Figure13illustrates the approach we are developing for highway environments (in cooperation with Toyota)[START_REF]High-Speed Highway Scene Prediction Based on Driver Models learned from Demonstrations[END_REF][START_REF]Interaction-Aware Tracking and Lane Change Detection in Highway Scenarios Using Realistic Driver Models[END_REF][START_REF] Sierra-Gonzalez | Modeling Driver Behavior from Demonstrations in Dynamic Environments Using Spatiotemporal Lattices[END_REF][START_REF] Gonzalez | Human-Like Decision-Making for Automated Driving in Highways[END_REF].

Figure

  Validation & Certification using Co-simulation & Formal Methods. A few years ago, we started to address the problems of Validation & Certification of "on-board Perception & Decision-making components" of future ADAS & AD 31

Figure 14 :

 14 Figure 14: Inria & IRT Nanoelec experimental platforms & Testing facilities (Road intersection, Connected Road Side Unit, Mannequin …).

2

  Nanoelec, for addressing the challenge of the development of embedded solutions for our Bayesian Perception & Decision-making approach. In 2017, my former student Tiana Rakotovao proposed a clever solution to this problem in his PhD thesis; this research work has been done under my guidance and the co-guidance of Diego Ruspini (Engineer at CEA), in the scope of our IRT Nanoelec project. This pioneer research work on the "Embedded Bayesian Perception paradigm" has given rise to (See achievements 4 & 5 for more details): -Two PhD theses: Christophe Coué (initial concept, 2004) and Tiana Rakotovao (embedded solutions, 2017). -Numerous publications in major IEEE conferences & journals, and some publications in International Conferences in the field of "Embedded Systems and Software" (e.g. Design Automation Conference). -An embedded Bayesian Perception prototype exhibited at CES 2018. -Several patents on the Bayesian Perception basic concepts and on their applications to the next generation of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving Systems. -Creation of our startup Probayes SA (2003) & Technological transfer to Probayes SA (2006).-Licenses of our Bayesian Perception software CMCDOT recently sold to several industrial companies: Toyota and two other companies (confidential). -Several bilateral industrial contracts (Toyota Europe, Denso Japan, EasyMile).

o Prediction of the most likely future motions of the observed entities (robots, vehicles, pedestrians ...) and collision risk assessment. The

  During the period 2009-20, we have designed and developed two complementary types of approaches for addressing this issue:

	basic models vas developed during the PhD theses of my former students Dizan Vasquez (2009),
	Christopher Tay Meng Keat (2009), Stéphanie Lefevre (2012). Two complementary approaches have been developed and
	patented respectively with Toyota and with Renault.
	Recent work on this research line is focusing on the use of new approaches combining Machine Learning and AI-based
	models. This new research activity has given rise to several international publications, to the filing of several patents, and to
	the recent defense of the two PhD theses: David Sierra-Gonzalez (2019) and of Mathieu Barbier (2019).

o Decision making for a safe and socially acceptable navigation of a robot moving among Human being. The basic underlying models have been developed in the scope of the PhD theses of two of my former students: Chiara Fulgenzi

	(2009)
	and Jorge Rios-Martinez (2013). I recently stopped to work on the "social aspect" of the navigation process, which is now
	addressed in our Inria team-project Chroma by Anne Spalanzani. My focus is now on the "motion safety" and "human
	driver's behaviors modelling" issues, with an emphasis on Machine Learning approaches.
	During the period 2009-20, this work has given rise to (see achievements 4 & 5 for more details):
	-Numerous publications in high impact conferences and journals (IJRR, IEEE Trans on ITS, IEEE ITSM, IEEE ICRA,
	IEEE/RSJ IROS, IEEE IV, ISRR), and numerous invited talks.
	-7 PhD theses (Dizan Vasquez, Christopher Tay Meng Keat, Chiara Fulgenzi, Jorge Rios-Martinez, Stéphanie Lefevre, David
	Sierra-Gonzalez, Mathieu Barbier).
	-A book chapter in the Handbook of Intelligent Vehicle (2012).
	-Several Scientific Awards related to this recent research line.
	-Four patents registered during the period 2010-13: Inria & Toyota (2010), Inria & Renault (2012), Inria (2013), Inria &
	Berkeley (2013).

This research line is still at the heart of my research program for the next 5 years, with a strengthening of the knowledge & learning based components. See part 2 for more details. 5. Achievements 4: Publications & Scientific Information Dissemination & Scientific Awards Dr

  . Christian Laugier has authored or co-authored more than 400 scientific publications in peer-reviewed international journals (~10%) and in peer-reviewed conferences & workshops. He also co-authored 6 books chapters and about 20 patents & registered software packages. In addition, he gave around 35 guest keynote, plenary lectures and tutorials. Most of these publications and guest lectures have been done within the framework of the major IEEE Conferences or Journals in the fields of Robotics & Automation (IEEE RAS) and of Intelligent Transportation Systems (IEEE ITSS). => The list of publications & achievements for the period before 2009 is available on my old home-page http://emotion.inrialpes.fr/laugier and on the webpage https://hal.inria.fr/hal-03091861 => The list of the significant publications & invited talks for the periods 2006-20 is available in the last part of this report.

Brief overview of the Publication & Scientific information dissemination achievements: -Book edition & Journal special issues: Contribution to the edition of 2 Handbooks 6 and co-edition of 3 Books 7 in

	the fields
	of Robotics and of Intelligent Transportation Systems. Co-edition of several Special Issues in high impact scientific journals
	such as IJRR, JFR, Advanced Robotics, IEEE RAM, IEEE T-ITS or IEEE ITSM.
	-

Guest Keynote & Plenary lectures in IEEE conferences: Numerous

  Guest Keynote & Plenary lectures in some major IEEE-RAS and IEEE-ITS conferences8 and in smaller IEEE conferences9 and in workshops organized in the scope of IEEE/RSJ IROS and IEEE ICRA conferences. Numerous invited papers & talks in IEEE International Conferences or Symposiums 10 and in various international workshops or seminars (China, Japan, Singapore, Korea, Thailand, Mexico, Brazil, Syria, Canada, USA, etc.). -

-Invited conference papers & talks:

Popular publications, speeches and media events:

  Several popular publications on "Robotics problems & solutions" and participation to several media events in the fields of Robotics and Autonomous Vehicles 11 .

-International tutorials: About 20 tutorials on Robotics and 5 recent tutorials on Autonomous Vehicles Technologies

12

have been done in the scope of summer schools, international conferences, or international cooperation programs. In addition, a MOOC entitled "mobile robots and autonomous vehicles" and including a chapter on "Bayesian Perception" have been broadcasted

3

times in 2015-16 by Inria-uTOP; an online version is available since 2017. cel-01256021 -International Scientific Events: Strong involvement in several Editorial Boards of IEEE conferences & journals 13 , in numerous Organizing Committees of IEEE conferences & workshops & forums 14 , and in several IEEE Awards Committees.

  4 startups in the period 1982-92 (ITMI, Getris-Images, Aleph Technologies, Aleph Med) and the startup (Probayes SA, 2003) for the commercialization of technologies developed within the framework our recent research line. The startup Probayes SA is still very active and was recently acquired by the industrial group "La Poste"; Scientific Advisor for Probayes since 2008.

. -Best student Paper Award at IEEE IV 2012 to my former PhD student S. Lefevre (co-authors: C. Laugier & J. Ibanez-Guzman). -IEEE RAS TC Awards 2006 (C. Laugier, A. Broggi , U. Nunes) & 2013 (C. Laugier, Ph Martinet, C. Stiller) -IEEE/RSJ IROS Harashima Award for Innovative Technologies (C. Laugier, 2012) 15 . -IEEE/RSJ IROS Fellow (C. Laugier, 2016). -IEEE/RSJ IROS Distinguish Service Award (C. Laugier, 2016) -Finalist EU Robotics Technology Transfer Award 2014 (C. Laugier, Inria & K. Mekhnacha, Probayes SA) 16 . Springer Handbook of Robotics 2 nd edition (chapter "Intelligent Vehicles", July 2016) & Springer Handbook on Intelligent Vehicle (chapters on "Fully Autonomous Driving", March 2012). Both handbooks are still widely disseminated and referenced. Books: Springer STAR on "Probabilistic Reasoning and Decision Making in Sensory-Motor Systems'" (P. Bessiere, C. Laugier, R. Siegwart, 2008); Springer STAR on "Autonomous Navigation in Dynamic Environments" (C. Laugier, R. Chatila, 2007); Springer Lecture notes in Computer Science on "Geometric Reasoning for Perception and Action" (C. Laugier, 1993). IEEE-RAS: IROS 2014 & 2019, ICARCV 2008 & 2014 & 2018 -IEEE-ITS: ITSC 2002 FSR 2009, ARSO 2016, Workshop @ ECCV 2018, RWIA 2018, WRC 2019, etc. ISRR, WAFR, ICARCV, ICIT, Workshops at ICRA or IROS, etc. Recent media events on Autonomous Vehicles: « Assistance à la conduite & Conduite autonome » (Magazine Industrie & Technologies, Sept 2015), « Véhicule autonome et Intelligence artificielle » (Interview & vidéo, TV Chanel Sciences et Vie », Sept 2017), « Le véhicule autonome de demain » (Interview, France Bleu Isère, diffusé le 11 Octobre 2018). EU Emaro Master (ECN Nantes 2015 & 2016), EU Summer School on IV (Compiegne 2017), BIT Beijing (2017), Winter School on AI (2018). These tutorials include a chapter describing the fundamentals of our Bayesian approach. IEEE Conferences: ICRA, IROS, IV, etc. -IEEE Journals: T-IV, T-ITS, Robomech, ISR, etc. IEEE Conferences: ICRA, IROS, IV, FSR, ICARCV, etc. -Workshops & Forums: ICRA, IROS, IV, ICARCV, etc. Harashima Award 2012 for "Outstanding contributions to embedded perception and driving decision for intelligent vehicles". EU Technological Transfer Award 2014 for "Transfer of Bayesian Perception and decision-making technologies in industrial applications". 6. Achievements 5: Innovation & Valorization, Startups o Startups (founding member & scientific advisor):

CEA: Commissariat à l'énergie atomique et aux énergies alternatives (French R&D Institute)

Pioneer work on the innovative concept of "Park Assist" for cars (see https://en.wikipedia.org/wiki/Automatic_parking), but the patent application has been cancelled because of a previous partial disclosure(our ICRA 1996 & IV 1996 publications). Consequently, the transfer of this technology to Toyota has been stopped in 1997, and Toyota developed and commercialized its own "Park Assist" product in 2004. See the following video for illustration (video proceedings IROS 1997): https://hal.inria.fr/hal-03088658

This technology is part of the ProBT library commercialized by Probayes SA.

IRT: French Technological Research Institute -SVA project: Long-term R&D program on "Safety of Autonomous Vehicles".

Scientific Research Position at Inria.

The Bayesian programming formalism, initially developed in the eMotion team, pioneered (together with the contemporary work of Thrun, Burgard and Fox) a systematic effort to formalize robotics problems under the Probabilistic Theory. This approach is now pervasive in Robotics.

CMCDOT: Conditional Monte Carlo Dense Occupancy Filter.

C. Sakaridis, D. Dai, and L. V. Gool, "Semantic Foggy Scene Understanding with Synthetic Data," IJCV 2018.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR, 2018.

Conservative short-term prediction: Motion prediction under the assumption that motion parameters are assumed to be stable for a short period of time (typically 3-5 seconds ahead). However, if there is an unexpected dynamic change in the environment during this period of time, the "prediction model" can be updated in less than 40ms.

The time horizon considered ranges from a few seconds (e.g. typically 3-5 seconds for the detection of unexpected events), to a longer time interval to generate more elaborate maneuvers taking into account contextual information as well as behavioral and interaction models.

Semantic Collision Risk: Nature & Severity of the collision risk, Dynamics & Time parameters, Avoidance strategies, etc.

Traffic scenarios in which autonomous vehicles, cars driven by humans and other traffic actors (e.g. pedestrians, cyclists, etc.) share the same space (and therefore interact with each other).

"Safety of Autonomous Vehicles". 3 Inria project-team "e-Motion", created in 2004 after a one year period of incubation of the initial idea. This research team was common to Inria Grenoble Rhône-Alpes and to the LIG Laboratory of the University Grenoble-Alpes (UGA). During the period 2004-14, the e-Motion project-team has received excellent feedbacks from several international evaluation panels

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