Titre : Développement d'un système d'avertissement sonore, validé par EEG, basé sur des approches vision et acoustique pour la detection de véhicules approchants des véhicules moteur deux roues.

Mots clés : Sécurité des motocyclistes, Detection de collision, Analyse de signaux EEG Résumé : Dans de nombreux pays, le taux de mortalité des motocyclistes est beaucoup plus élevé que celui des autres conducteurs de véhicules. Parmi de nombreux autres facteurs, les collisions arrière des motocyclettes contribuent fortement à ces décès de motards. Les systèmes de détection de collision peuvent être utilisés pour minimiser ces accidents mortels. Cependant, la plupart des systèmes de détection de collision existants n'identifient pas le type de danger potentiel auquel sont exposés les motocyclistes. Chaque système d'alerte de collision utilise une technique de détection de collision distincte, ce qui limite ses performances et rend impératif l'étude de son efficacité. Malheureusement, aucun travail de ce type n'a été signalé dans ce domaine particulier pour les motocyclistes. Par conséquent, il est important d'étudier la réponse physiologique du motocycliste contre ces systèmes d'alerte de collision. Dans cette recherche, une méthode de détection et de classification des véhicules approchant (par l'arrière) est présentée. Pour la détection de collision, une approche basée vision et une technique basée sur l'étude des données sonores ont été utilisées. Pour les techniques visuelles et acoustiques, des caractéristiques d'apparence et de spectre de puissance ont été utilisées, respectivement, pour détecter le véhicule qui s'approche à l'extrémité arrière de la motocyclette. En ce qui concerne la classification des véhicules, seule une technique basée acoustique est utilisée; un spectre de puissance acoustique et des caractéristiques énergétiques sont utilisés pour classer les véhicules qui approchent.

Keywords : Motorcyclist safety, Collision detection, EEG analysis

Abstract : In many countries, motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. Collision detection systems can be used to minimize these fatalities. However, most of the existing collision detection systems do not identify the type of potential hazard faced by motorcyclists. Unfortunately, motorcyclists' physiological response has not been investigated for the verbal warning-based collision warning system. Furthermore, motorcycles have different maneuverability as compared to cars and other four-wheeler vehicles, which can affect the riders' response for this collision warning system. Therefore, it is important to study the physiological response of the motorcyclist for this system. In this research, a rear end vehicle detection and classification method is presented for motorcyclists. For visual-based and acoustic-based collision detection techniques, appearance features and power spectrum have been used, respectively. As for the vehicle classification, only an acoustic technique is utilized; an acoustic power spectrum and energy features are used to classify the approaching vehicles. Two types of datasets which are comprised of self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and online datasets (for vision-based vehicle detection and for audio-based vehicle classification techniques) are used for validation. Moreover, an event-related potential (ERP) based physiological study has been performed on motorcyclists to investigate their responses towards the rear end collision warning system. Two types of auditory warnings (i.e., verbal and buzzer) are used for this warning system. To study the response of the motorcyclists, the N1, N2, P3, and N400 components have been extracted from the electroencephalography (EEG) data. The proposed methodology successfully detected and classified the vehicle for self-recorded datasets and achieved a true positive rate of 95.87% and a false detection rate of 5.25%. For online datasets, the higher true positive rate and less false detection rate have been achieved as compared to the existing state of the art methods. The introduced auditory warnings have shown positive effects at neural levels on motorcyclists and reduce their reaction time and attentional resources required for processing the target correctly. In summary, the proposed rear-end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible collisions.

Université Bourgogne Franche-Comté 32, avenue de l'Observatoire iv 4.4: Four classes' detection by using F7 from online dataset ESC-50 [START_REF] Piczak | ESC: Dataset for environmental sound classification[END_REF]. .. techniques are used to detect and classify the vehicles approaching at the rear end of the motorcycle. Also, the response of motorcyclists is investigated by measuring the variations in event-related potential (ERP) components. It has been found that the auditory collision warning system had a positive effect on neural levels at the motorcyclist.

This chapter discusses the motorcyclist fatalities and some safety techniques (including collision detection systems) that are used to increase the safety of the motorcyclists. It focuses on the audio/vision-based vehicle detection/classification techniques that are either used in collision detection system or can be used for motorcyclist safety enhancement. In addition, some information is provided about the driver's response studies that validate these collision detection systems. Furthermore, problem statements, hypothesis, objectives and scopes of the present research work are also included in this chapter.

Motorcyclist Accidents

According to the motorcycle industry, there were 313 million motorcycles on the road as of the year 2012, of which 78% are in Asia, 5% in Latin America, and 2% in North America [START_REF] Muslim | A review on retrofit fuel injection technology for small carburetted motorcycle engines towards lower fuel consumption and cleaner exhaust emission[END_REF]. The term motorcycle is interchangeable with the term motorbike. However, the term motorbike is mostly used in the United Kingdom and Australia. While in the United States a motorbike has less power as compared to the motorcycle vehicle.

Globally, the term motorbike is used less than the term motorcycle. World Health Organization (WHO) reported that annually 1.25 million people die due to road fatalities [START_REF] Organization | Global status report on road safety 2015[END_REF]. Motorcyclists account for 24.1% of these fatalities as compared to the other modes of transport [START_REF] Manan | Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia[END_REF]. However, Motorcycle fatalities are a major concern in many Asian countries. In Malaysia, for example, around 62% of road fatalities are motorcyclist fatalities [START_REF] Ariffin | Risk factors identification and issues pertaining to road collisions involving pedestrian and motorcycle[END_REF]. This figure is 66.6% in Cambodia, 73.5% in Thailand, 46.1% in Singapore, and 35.7% in Indonesia [START_REF] Manan | Factors Associated with Motorcylists' Safety at Access Points along Primary Roads in Malaysia[END_REF].

In many other countries around the world, the situation is not as bad but still needs to be addressed. In New Zealand, for example, 13% of road fatalities are motorcyclist fatalities [START_REF] Helman | The effect of two novel lighting configurations on the conspicuity of motorcycles: a roadside observation study in New Zealand[END_REF]; while, this figure is 9% in the Europe [START_REF] Organization | Global status report on road safety 2015[END_REF]. According to the U.S. National Highway Traffic Safety Administration (NHTSA) in the year 2014, motorcyclist fatalities account for the 14% of road fatalities [START_REF] Noland | Has the great recession and its aftermath reduced traffic fatalities?[END_REF].

Motorcyclists also sustain many types of injuries that are more serious than those suffered by other vehicle drivers in accidents [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF]. Head injuries are more common among motorcyclists and contribute to more than half of all fatalities among riders. Chest and abdominal injuries are the second leading cause of motorcyclist fatalities, accounting for 7% to 25% of motorcycle deaths, while cervical spinal injuries often occur in fatal motorcycle accidents. Nonfatal motorcycle accidents mostly involve lower-extremity injuries. Among lower-extremity injuries, fractures are the most common and can lead to permanent disability [START_REF] Lin | A review of risk factors and patterns of motorcycle injuries[END_REF]. Soft-tissue injuries and neck injuries also occur in motorcycle accidents [START_REF] Rahman | Burden of motorcycle-related injury in Malaysia[END_REF].

Motorcycles have characteristics different from those of other vehicles, especially in terms of their maneuverability. Motorcyclists require high levels of coordination and balance to control them [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF]. Motorcyclists are more exposed to the external environment; therefore, more likely to suffer injuries or fatalities compared to the drivers of cars and other vehicles [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF][START_REF] Trinh | Motorcycle Helmet Usage among Children Passengers: Role of Parents as Promoter[END_REF]. Motorcycles frontal and rear end collisions have highly contributed to the motorcyclist fatalities and are mostly recorded on motorways and primary roads. Many factors are involved in motorcycle accidents: among them are over speeding, driving under the influence of alcohol, ignorance of the route, and loss of controls [START_REF] Manan | Factors Associated with Motorcylists' Safety at Access Points along Primary Roads in Malaysia[END_REF][START_REF] Manan | Factors associated with motorcyclists' speed behaviour on Malaysian roads[END_REF].

Motorcyclist Safety Systems

To increase the safety of the motorcyclists and minimize their fatalities, different techniques have been proposed; they can be divided into two major categories: 1) passive safety and 2) active safety. Passive safety covers the use of helmets [START_REF] Abbas | Does wearing helmets reduce motorcycle-related death? A global evaluation[END_REF][START_REF] Demarco | The impact response of motorcycle helmets at different impact severities[END_REF], special cloths [START_REF] De Rome | Motorcycle protective clothing: protection from injury or just the weather?[END_REF], airbags [START_REF] Ariffin | Exploratory study on airbag suitability for low engine capacity motorcycles[END_REF], etc. Active safety covers antilock braking system (ABS) [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF], electronic stability control [START_REF] Kalaiselvan | Design of Stability Control Motorbike with Abs and Crash Location Sensing[END_REF][START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF], and advance collision warning (ACW) systems for motorcycles [START_REF] Bekiaris | SAFERIDER Project: new safety and comfort in Powered Two Wheelers[END_REF][START_REF] Biral | An intelligent curve warning system for powered two wheel vehicles[END_REF][START_REF] Fang | A vision-based safety driver assistance system for motorcycles on a smartphone[END_REF], etc.

In passive safety systems, helmets are found effective to minimize the head injuries and minimize the chance of motorcyclist death up to 40% [START_REF] Keng | Helmet use and motorcycle fatalities in Taiwan[END_REF][START_REF] Savolainen | Probabilistic models of motorcyclists' injury severities in single-and multi-vehicle crashes[END_REF]. Full-face helmets are found safest among all types of helmets. Furthermore, protective clothes in passive safety systems reduce the risk of the injury to the upper body, hands, wrists, legs, feet, and ankles [START_REF] De Rome | Motorcycle protective clothing: protection from injury or just the weather?[END_REF]. Also, wearing reflective protective clothing can further reduce the risk of accidents [START_REF] Sorbie | Angels on the Catwalk?[END_REF][START_REF] Allen | Contributing factors to motorcycle injury crashes in Victoria, Australia[END_REF]. However, the installation of airbags on a motorcycle is still a major challenge. This is due to the direct exposure of the motorcyclist to the external environment and the absence of seat belts in motorcycles [START_REF] Rogers | Factors and status of motorcycle airbag feasibility research[END_REF]. The research is still on ongoing to find the suitable location for the installation of an airbag on a motorcycle [START_REF] Kobayashi | Crash detection method for motorcycle airbag system with sensors on the front fork[END_REF][START_REF] Aikyo | Study on Airbag Concept for Motorcycles Using Opposing Vehicle as Reaction Structure[END_REF].

These passive safety techniques aim to reduce injuries; while, active techniques help to avoid accidents from occurring in the first place. The active safety systems are not yet fully developed and still are at a research stage. In, active safety systems, ABS and ESC systems were found reliable to increase the stability of the motorcycle [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF][START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF]. ABS system increases the braking performance for both dry and wet road surfaces and prevents the motorcycle from tipping over [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF]. Similarly, ESC system detects different parameters such as speed, yaw rate, and wheel torque, etc., to detect and control the motorcycle trajectory [START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF]. However, for motorcycle rear-end collisions both systems were found less effective.

An autonomous braking system has also been presented to increase the safety of motorcycles [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF][START_REF] Giovannini | Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system[END_REF]. If there are chances of collisions, these systems automatically apply the brakes. However, these systems rely on the collision detection methods and very few collision detections or collision avoidance methods for motorcycles have been reported in the literature [START_REF] Bekiaris | SAFERIDER Project: new safety and comfort in Powered Two Wheelers[END_REF][START_REF] Biral | An intelligent curve warning system for powered two wheel vehicles[END_REF][START_REF] Fang | A vision-based safety driver assistance system for motorcycles on a smartphone[END_REF]. The collision detection methods proposed in the literature were not fully developed [START_REF] Bekiaris | SAFERIDER Project: new safety and comfort in Powered Two Wheelers[END_REF][START_REF] Biral | An intelligent curve warning system for powered two wheel vehicles[END_REF]. Furthermore, in [START_REF] Fang | A vision-based safety driver assistance system for motorcycles on a smartphone[END_REF], a mobile camera needed to be installed manually for every driving session. Thus, it may change the angle of the mobile camera installed on a motorcycle and may affect the vehicle detection.

Related Collision Detection Systems

Every collision warning system used a discrete collision detection technique.

These systems first detect the approaching vehicles. If there are chances of a collision, these systems give early warning to the driver to avoid it. The collision detection methods for cars and other four-wheeler vehicles rely on different types of sensors and approaches to detect the vehicles [START_REF] Ghatwai | Vehicle to vehicle communication for crash avoidance system[END_REF][START_REF] Zhang | A method for connected vehicle trajectory prediction and collision warning algorithm based on V2V communication[END_REF][START_REF] Meinl | An experimental high performance radar system for highly automated driving[END_REF][START_REF] Zhang | Efficient L-shape fitting for vehicle detection using laser scanners[END_REF][START_REF] Jiménez | Vehicle tracking for an evasive manoeuvres assistant using low-cost ultrasonic sensors[END_REF][START_REF] Kuo | Vision-based vehicle detection for a driver assistance system[END_REF][START_REF] Sivaraman | Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis[END_REF]. Among different types of sensors, cameras and microphones are relatively cheap and produce reliable results as compared to the other sensors such as radar, ultrasonic, a global positioning system (GPS) and laser scanners, etc.

The cameras provide a wide field of view, thus allowing the detection of the vehicles across multiple lanes. There are many vision-based techniques that used the appearance features of the approaching vehicle for collision detection [START_REF] Kuo | Vision-based vehicle detection for a driver assistance system[END_REF][START_REF] Arenado | Monovision-based vehicle detection, distance and relative speed measurement in urban traffic[END_REF][START_REF] Tsai | Vehicle detection using normalized color and edge map[END_REF][START_REF] Teoh | Symmetry-based monocular vehicle detection system[END_REF][START_REF] Kalinke | A texture-based object detection and an adaptive model-based classification[END_REF][START_REF] Wang | Driver assistance system for lane detection and vehicle recognition with night vision[END_REF][START_REF] Wong | Low relative speed moving vehicle detection using motion vectors and generic line features[END_REF].

Among different appearance-based features, edges features are found more suitable option (i.e., the vehicle rear or frontal views show strong vertical/horizontal edges and corners) to detect the approaching vehicles. Yet, the vehicle detection techniques presented in the literature have very low frame rate (i.e., the process only a few frames in one second). Vibrations are more prominent in motorcycle compare to fourwheeler (and higher) vehicles. Therefore, a fast and reliable vehicle detection technique for motorcycles using visual information is still a challenge.

It is believed that the information about potential hazard types will further assist the driver to understand the situation and can be helpful to avoid potential accidents.

However, in the literature available vision-based vehicle classification techniques are limited to surveillance applications [START_REF] Hussain | On-road vehicle classification based on random neural network and bag-of-visual words[END_REF][START_REF] Shantaiya | Multiple class image-based vehicle classification using soft computing algorithms[END_REF][START_REF] Moussa | Vehicle type classification with geometric and appearance attributes[END_REF][START_REF] Xiang | An Effective and Robust Multi-view Vehicle Classification Method Based on Local and Structural Features[END_REF]. Many challenging issues still exist in vehicles classification. That include road environment, illumination variations, camera field view, the similarity in vehicle appearance and a large number of vehicle types, etc. [START_REF] Hussain | On-road vehicle classification based on random neural network and bag-of-visual words[END_REF][START_REF] Abdulrahim | Traffic surveillance: A review of vision based vehicle detection, recognition and tracking[END_REF]. Variations of light, weather and day/night scenario make it even more complex to detect the vehicle type from the images.

The microphones are low cost as compared to other types of vehicle detection sensors. Also, in contrast to visual features, vehicle sound spectrum obtained by the microphone is not affected by the light intensity [START_REF] Toyoda | Traffic monitoring with ad-hoc microphone array[END_REF][START_REF] Shah | Classification of vehicles using adaptive neuro fuzzy inference system[END_REF]. Every vehicle that moves on the road generates acoustic spectrum dominated by the engine noise, exhaust noise, tire noise and air turbulence [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF]. This sound spectrum can be used to detect and classify the vehicles. Therefore, it is important to investigate it further. In the literature, the methods have been reported for audio based vehicle detection and classification for surveillance applications [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Rahim | Heterogeneous multi-classifier for moving vehicle noise classification[END_REF][START_REF] Kubera | Audio-based hierarchic vehicle classification for intelligent transportation systems[END_REF][START_REF] Alexandre | Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications[END_REF][START_REF] Mayvan | Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors[END_REF][START_REF] Paulraj | Moving vehicle recognition and classification based on time domain approach[END_REF].

There is a significant difference between vehicle detection/classification for collision detection system and for surveillance application. For surveillance application, microphones mostly fixed at some points are used to detect and classify the vehicles [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Rahim | Heterogeneous multi-classifier for moving vehicle noise classification[END_REF][START_REF] Kubera | Audio-based hierarchic vehicle classification for intelligent transportation systems[END_REF][START_REF] Alexandre | Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications[END_REF][START_REF] Mayvan | Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors[END_REF][START_REF] Paulraj | Moving vehicle recognition and classification based on time domain approach[END_REF]. Also, the air turbulence can increase false vehicle detections for the collision detection system as it appears after the vehicle passes the microphone [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF]. Therefore, for the collision detection/warning system, it is still challenging to extract features from the audio signal (obtained by using a single microphone) to detect and classify the approaching vehicle by neglecting the audio of the vehicles moving opposite lane with minimum air turbulence.

By using a single microphone, it is not possible to find the rear end approaching vehicle location for a collision warning system because of the Doppler shift in an audio spectrogram can change due to the movement of any vehicle [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF]. Hence, vision-based vehicle detection can be used with an audio-based vehicle detection/classification method, to increase the accuracy and reliability of the collision detection/warning system.

Response of Drivers against Collision Warning Systems

Because of the technical constraints, these collision warning systems can prone to false alarms or nuisance alarms and misses some vehicles. The collision warning system with such limitations can impact the driver's behavior in two ways: 1) it can distract the drivers or 2) it can redirect the driver's attention to the road if necessary. It is expected that these systems would benefit the drivers by diverting their attention towards the road and assisting them to understand the critical situation.

Many researchers have investigated the response of car drivers against collision

warning systems [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF][START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF][START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF][START_REF] Scott | A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving[END_REF][START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF][START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF][START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF]. However, no such work has been reported for motorcyclists.

Motorcycles have different maneuverability as compared to cars and other vehicles [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF]. Also, motorcyclists are more exposed to the external environment and are more likely to get distracted as compared to the drivers of cars and other vehicles. They require high levels of coordination and balance to control motorcycle, particularly for scenarios when they trying to avoid possible rear-end collisions [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF]. Therefore, it is important to investigate that the design collision warning system can be useful for motorcyclists or it will create a further distraction.

In these collision warning systems, different types of warnings are used such as visual [START_REF] Kazazi | Accident prevention through visual warnings: how to design warnings in head-up display for older and younger drivers[END_REF], auditory [START_REF] Bella | Effects of directional auditory and visual warnings at intersections on reaction times and speed reduction times[END_REF], vibratory [START_REF] Meng | Dynamic vibrotactile signals for forward collision avoidance warning systems[END_REF], or tactile [START_REF] Meng | Tactile warning signals for in-vehicle systems[END_REF], etc. The audio warnings can be further divided into buzzer warnings and auditory verbal warnings. Auditory verbal warnings convey the information about the situation or about the potential hazard e.g., its type and distance from the source, etc. [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF]. These response studies can be divided into two categories: 1) behavioral studies and 2) electroencephalography (EEG) based response study of drivers. All of the above studies found that the collision warning systems increased the alertness of the drivers. Also, in these research studies, there were pre-crash scenarios; therefore, the simulator was utilized for the safety of drivers [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF][START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF][START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF][START_REF] Scott | A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving[END_REF][START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF][START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF][START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF].

In behavioral research studies, different parameters (i.e., drivers' reaction time, brake time and time to the collision, etc.,) are used to find the alertness of vehicle drivers [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF][START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF][START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF][START_REF] Scott | A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving[END_REF][START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF][START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF][START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF]. In these studies, visual, auditory, tactile or combination of these warnings was used to alert the drivers [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF][START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF][START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF][START_REF] Scott | A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving[END_REF][START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF][START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF][START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF]. Tactile warnings have been found to be more suitable as compared to any other types of warnings [START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF]. Also, auditory warnings were found more convenient as compared to the visual warnings. The auditory-verbal warnings are considered less annoying for the critical driving situation [START_REF] Politis | To beep or not to beep?: Comparing abstract versus language-based multimodal driver displays[END_REF]. The driver can swiftly learn these warnings. Also, these warnings can convey spatial information about the potential hazard, thus may reduce the perception and response time to the danger [START_REF] Meng | Tactile warning signals for in-vehicle systems[END_REF].

Also, many researchers investigated the physiological response of drivers through electroencephalography (EEG) [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF][START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF]. However, tactile warnings were not used for these EEG studies, as movements or motion can affect EEG signals. EEG alpha and theta bands were used to study the response of drivers. In these studies, when the auditory warnings were given to the drivers, their alpha/theta signals power decreased and they responded rapidly to avoid potential collisions [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF][START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF]. These warnings were also found effective to minimize the drowsiness effects [START_REF] Huang | An EEG-based fatigue detection and mitigation system[END_REF][START_REF] Berka | Implementation of a closed-loop real-time EEG-based drowsiness detection system: Effects of feedback alarms on performance in a driving simulator[END_REF].

Besides alpha and theta bands in EEG studied, the event-related potential (ERP) components were also used to examine the responses of drivers for collision warning systems [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF][START_REF] Khaliliardali | Detection of anticipatory brain potentials during car driving[END_REF]. These ERP components arise due to the specific sensory, cognitive, or motor event, etc. In these studies, ERP components named as contingent negative variation (CNV), N1, N2, and P3, were used to find the alertness of the drivers. The CNV was one of the first ERP components to be described in the year 1964. The negative CNV peak rises quickly if the subject is uncertain about when the imperative stimulus will be, and it rises gradually if the subject is confident about when the imperative stimulus will occur [START_REF] Tecce | Contingent negative variation (CNV) and psychological processes in man[END_REF].

Similarly, P300 is considered as an endogenous potential, as its occurrence links with the person's reaction. More specifically, the P300 is thought to reflect processes involved in stimulus evaluation or categorization. When recorded by EEG, it surfaces as a positive deflection in voltage with latency (delay between stimulus and response) of roughly 250 milliseconds to 400 milliseconds [START_REF] Polich | Updating P300: an integrative theory of P3a and P3b[END_REF]. N1 is a negative-going evoked potential measured by EEG and it peaks in-betweens 80-120 milliseconds after the onset of a stimulus and distributed mostly over the fronto-central region of the scalp.

While N2 peaks occur 200-350 milliseconds post-stimulus and are found primarily over anterior scalp sites.

In these EEG studies, drivers' response time for different types of collision warning systems (i.e., 100% and 70% accurate collision warning systems) were compared to the condition when the collision warning system was absent. It has been observed that these warning systems decreased the reaction time and increased the alertness of the drivers. Also, variations in the amplitude and latency of N1, N2, and P3 components have been found for these warning systems compared to the no warning condition. These variations occurred due to the collision warning system facilities at the sensory level and the driver required less time and the attentional resources for processing the target correctly.

Research Gaps and Motivation for Research

Among Also, some limitations are found in these behavioral and EEG/ERP based drivers' response studies. Most of these were reported for car drivers. No such work has been found for motorcyclists. The response of motorcyclist may vary from the car and other vehicle drivers. EEG studies give a better perception of the driver about the collision warning system. Yet, these studies used the buzzer warnings for the collision warning systems. The physiological response of the drivers may differ when auditory verbal warnings presented. These studies also focus on the frontal collision warning system. It is important to investigate the physiological response of motorcyclists or other vehicle drivers (by using ERP components) for the rear collision warning system. Besides, N1, N2 and P3 components, N400 also needs to be investigated for auditory verbal warnings. ERP component N400 is part of the normal brain responses to verbal words and other meaningful stimuli. It peaks occur 400-500 milliseconds post-stimulus [START_REF] Ortega | Voluntary modulations of attention in a semantic auditory-visual matching Task: an ERP study[END_REF].

Problem Statement

Based on the above research gaps, the problem statement can be formulated as follows:

• A fast and reliable rear end vehicle detection approach for motorcycles using appearance features need to be investigated. Also, audio and visual datasets have different frame rate; therefore, the integration of visual technique with audio-based vehicle detection and classification

technique is yet to be designed.

• Further analysis of the acoustic power spectrum (obtained by using a single microphone) is required to detect and classify the approaching vehicles for a collision warning system.

• All previous physiological studies were limited to the frontal collision scenarios and only computed the N1, N2, and P300 for car drivers.

Moreover, only buzzer warnings were used to alert the car drivers. It is still challenging to investigate motorcyclists' physiological responses for the rear collision warning system for auditory verbal warnings by using ERP components N1, N2, P3, and N400, respectively.

Hypothesis

From the above problem formulation, we hypothesize that,

I.

The vehicles moving on the road have very strong frontal edges (i.e., appearance-based features) in images that can be used to detect their presence and it can be integrated with an audio-based vehicle detection/classification method by creating audio frames.

II.

Acoustic power spectrum consists of engine, tire and exhaust noises of the moving vehicles, and this spectrum can be used to detect the vehicle presence and to differentiate this vehicle from other vehicles.

III.

The auditory-verbal commands are found to be easily understandable and less annoying. It is believed that verbal warning commands will enhance the brain neural activities and will be helpful to assist the drivers in a collision warning system.

Research Objectives

I.

To develop a method to detect approaching vehicles at the rear end of the motorcycle (even in the presences of vibrations) by using appearance features from images.

II.

To evaluate the acoustic spectrum of the approaching vehicles to detect their presence, classify them into different categories, and integrate with visionbased vehicle detection technique.

III.

To investigate the physiological responses of motorcyclists for the rear end collision warning systems when they are presented the auditory-verbal warnings.

Scope of Study

This research explores suitable audio and visual features for a motorcycle rearend collision detection system. In this research, rear end approaching vehicles and their type is detected to alert the motorcyclist about potential hazards. For this, audio and visual datasets are recorded at daytime in Ipoh-Lumut highway and inside UTP for different lighting conditions and road scenarios by using a 125cc motorcycle.

Also, the physiological responses of motorcyclists are investigated for the rear end collision warning system for the auditory-verbal warning messages. The EEG data has been recorded by using a simulator located at the CISIR lab, UTP Malaysia.

The selected subjects for this experiment were UTP students or staff members with valid motorcycle driving license. Afterwards, offline analysis of EEG data has been

Thesis Outline

The work presented in this thesis consists of a total of five chapters. In Chapter

Motorcyclists Safety and Active Safety Systems

Motorcycle fatalities are a major concern in many Asian countries. Different Passive safety systems for the motorcyclist include helmets [START_REF] Ramli | Motorcycle helmet fixation status is more crucial than helmet type in providing protection to the head[END_REF][START_REF] Bazargani | Determinants and barriers of helmet use in Iranian motorcyclists: a systematic review[END_REF][START_REF] Russo | Examination of factors associated with use rates after transition from a universal to partial motorcycle helmet use law[END_REF][START_REF] Dapilah | Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents[END_REF], protective clothing [START_REF] De Rome | Validation of the principles of injury risk zones for motorcycle protective clothing[END_REF][START_REF] Yan | Analysis Based on Impact Resistance of Motorcycle Clothing Fabric Performance[END_REF][START_REF] Mao | High performance textiles for protective clothing[END_REF][START_REF] De Rome | Thermal and cardiovascular strain imposed by motorcycle protective clothing under Australian summer conditions[END_REF], and airbags [START_REF] Rogers | Factors and status of motorcycle airbag feasibility research[END_REF][START_REF] Aikyo | Feasibility study of airbag concept applicable to motorcycles without sufficient reaction structure[END_REF][START_REF] Loftén | First Side Curtain Airbag for Commercial Vehicles[END_REF][START_REF] Bendjaballah | Numerical modelling and experimental analysis of the passenger side airbag deployment in out-of-position[END_REF]. While, active safety systems for motorcyclists include antilock braking system (ABS) [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF], electronic stability control (ESC) [START_REF] Kalaiselvan | Design of Stability Control Motorbike with Abs and Crash Location Sensing[END_REF][START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF], and advance collision warning systems [START_REF] Bekiaris | SAFERIDER Project: new safety and comfort in Powered Two Wheelers[END_REF][START_REF] Spelta | Smartphonebased vehicle-to-driver/environment interaction system for motorcycles[END_REF] that provide the motorcyclist with an early warning of a potential accident [START_REF] Song | An on-road evaluation of connected motorcycle crash warning interface with different motorcycle types[END_REF]. Details of active safety systems are given below.

Antilock Braking System

A brake is a crucial device for a motorcycle. A motorcycle is normally equipped with individual brake systems for the front and rear wheels. Motorcyclists control the front brake by hand and the rear brake using foot [START_REF] Basri | Analysis of Concurrent Brake Application for Underbone Motorcycle[END_REF]. The front-wheel brake provides great deceleration but can result in an unstable ride or a loss of control owing to a shift in the center of gravity. Meanwhile, the rear-wheel brake does not affect the stability of the motorcycle but yields less deceleration [START_REF] Kato | Combination of antilock brake system (ABS) and combined brake system (CBS) for motorcycles[END_REF].

To address the above issue, manufacturers have developed a braking system that essentially links the front and rear brake controls. This system applies a braking force to both wheels when either control is engaged and is referred to as a combined braking system. With a combined braking system, it is still possible that a wheel may lock during hard braking [START_REF] Teoh | Effectiveness of antilock braking systems in reducing motorcycle fatal crash rates[END_REF]. To solve this, ABS has been proposed [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF][START_REF] Teoh | Effectiveness of antilock braking systems in reducing motorcycle fatal crash rates[END_REF][START_REF] Rizzi | The combined benefits of motorcycle antilock braking systems (ABS) in preventing crashes and reducing crash severity[END_REF][START_REF] Rizzi | The effectiveness of antilock brake systems on motorcycles in reducing real-life crashes and injuries[END_REF].

An ABS has been introduced to a range of motorcycles since the late 1980s and use electronic controls to maintain wheel rotation and increase stability. For hard braking, an ABS uses electronic control for both wheels and prevents the motorcycle from tipping over [START_REF] Rizzi | The effectiveness of antilock brake systems on motorcycles in reducing real-life crashes and injuries[END_REF]. The use of this brake in a motorcycle can improve the braking performance on both dry and wet surfaces [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF]. ABSs have been found beneficial for the reduction of motorcyclist injuries from 34% to 39% in Europe and from 42% to 48% in real-world severe accidents [START_REF] Fildes | Evaluation of the effectiveness of anti-lock braking systems on motorcycle safety in Australia[END_REF]. However, the motorcyclist cannot rely on an ABS to prevent all types of accidents. For example, an ABS cannot prevent accidents in the case that the motorcycle is struck from behind by another vehicle [START_REF] Teoh | Effectiveness of antilock braking systems in reducing motorcycle fatal crash rates[END_REF]. Additionally, ABSs are fully developed and there is not much room for improvement in ABS design.

Recently, an autonomous braking system has been presented for motorcycles [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF][START_REF] Giovannini | Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system[END_REF]. The system first detects frontal vehicles and, if there is a chance of collision, automatically applies braking. However, both cited studies were limited to low-speed scenarios and the study conducted by G. Savino et al. [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF] was also limited to obstacles encountered in a straight line.

Electronic Stability Control

The stability of a motorcycle is different and much more complex than that of a car [START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF]. Being a single-track vehicle, a motorcycle can easily become unstable [START_REF] Islam | A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama[END_REF].

The stability of a motorcycle, therefore, plays a vital role when the motorcycle is ridden. To increase the stability of a motorcycle, many researchers have proposed electronic stability control (ESC) systems [START_REF] Kalaiselvan | Design of Stability Control Motorbike with Abs and Crash Location Sensing[END_REF][START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF][START_REF] Watanabe | The Effect of a New Stability Control on the Simulated Cornering Behavior of Motorcycles[END_REF][START_REF] Lich | Motorcycle stability control-the next generation of motorcycle safety and riding dynamics[END_REF]. ESC was initially suggested to increase the stability of cars and it is currently used in most commercial cars.

In the case of a motorcycle, an ESC system detects the speed, yaw rate, lateral acceleration and wheel traction to determine whether the actual course of the motorcycle is the same as the driver's intended course. If there is a divergence, the ESC system applies different braking pressures to the wheels to correct the motorcycle trajectory [START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF]. An ESC system can effectively stabilize the motorcycle and help to minimize accidents that usually occur because of the instability of the motorcycle. Because ESC has already been fully developed and is currently used, there is little room for its further improvement.

Advance Collision Warning Systems

Little work has been reported for motorcycles collision detection or collision warning systems as given in Table 2.1 below. E. D. Bekiaris et al. [START_REF] Bekiaris | SAFERIDER Project: new safety and comfort in Powered Two Wheelers[END_REF], proposed different techniques to warn the motorcyclist about the potential collisions but did not present the development and accuracy of these techniques. C. Spelta et al., [START_REF] Spelta | Smartphonebased vehicle-to-driver/environment interaction system for motorcycles[END_REF] presented a method of communication between different components of a motorcycle to increase safety. C.-Y. Fang et al. [START_REF] Fang | A vision-based safety driver assistance system for motorcycles on a smartphone[END_REF] proposed a vision-based collision warning system for a motorcycle to avoid accidents. The system uses a mobile camera to detect frontal vehicles and a GPS device to estimate the distance to a frontal vehicle. The major issue with this technique is the placement of the mobile phone on the motorcycle. To implement the presented technique, a motorcyclist needs to install the phone camera manually in each driving session. The angle of the mobile camera may, therefore, vary in different driving sessions, making it difficult to detect frontal vehicles.

Additionally, the mobile phone can be damaged as it is placed in an open environment.

Also, a laser scanner has been proposed to detect the frontal vehicles for an autonomous braking system [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF][START_REF] Giovannini | Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system[END_REF][START_REF] Savino | Design of the decision logic for a ptw integrated safety system[END_REF]. F. Giovannini et al. [START_REF] Giovannini | Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system[END_REF] used the model given in [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF] to detect a vehicle. Also, they did not present the accuracy of their vehicle detection; meanwhile, G. Savino et al. reported the accuracy of 100% in their 2009 [START_REF] Savino | Design of the decision logic for a ptw integrated safety system[END_REF] study and 98% in their 2012 [START_REF] Savino | Decision logic of an active braking system for powered two wheelers[END_REF] study. However, both studies were limited to low-speed straight-line detection. All the above studies more focused on the autonomous braking system rather than vehicle detection.

G. Kumarasamy et al. [START_REF] Kumarasamy | Rider assistance system with an active safety mechanism[END_REF], proposed a hybrid technique of detecting frontal collision for motorcycles. In their study, a camera and a range-based sensor were used to detect frontal vehicles. For tracking, a motion gyroscope sensor was placed on the motorcyclist's helmet. The system can detect the frontal vehicle and monitor the rider's deviation from the intended path. This technique used a range-based sensor for vehicle detection within a city. However, the range of the sensor was only 4 meters, which can lead to a collision. Additionally, the accuracy of the hybrid technique was not presented.

In [START_REF] Amodio | Design of a lane change driver assistance system, with implementation and testing on motorbike[END_REF], a lane changing system is presented for motorcyclists. Radar is used to detect the vehicles in adjacent lanes. Also, a camera is used to record the whole session, which afterward used to validate the results. However, the accuracy of this technique is not presented.

Very little work has been done in the field of collision warning systems for motorcycles. Similar to the case for other vehicles, different types of collision warning systems can be designed for motorcycles. The development of different systems will allow us to measure performance and costs.

Limitations of Existing Motorcycle Safety Systems

Among passive safety systems, helmets are the most useful in reducing the extent of head injuries and thus reducing the probability of the death of the motorcyclist [START_REF] Keng | Helmet use and motorcycle fatalities in Taiwan[END_REF][START_REF] Savolainen | Probabilistic models of motorcyclists' injury severities in single-and multi-vehicle crashes[END_REF]. However, many motorcyclists refuse to wear helmets owing to the weight and overheating effect of helmets [START_REF] Bazargani | Determinants and barriers of helmet use in Iranian motorcyclists: a systematic review[END_REF][START_REF] Faryabi | Evaluation of the use and reasons for not using a helmet by motorcyclists admitted to the emergency ward of shahid bahonar hospital in kerman[END_REF]. Therefore, further research can be performed to develop the lightweight and strong helmets with a coolant system to make it more acceptable for motorcyclists.

Protective clothing can be used by motorcyclists to minimize the chances of soft-tissue injury. The installation of airbags in a motorcycle remains challenging.

Research is ongoing to find the best location for the installation of an airbag in a motorcycle [START_REF] Kobayashi | Crash detection method for motorcycle airbag system with sensors on the front fork[END_REF][START_REF] Aikyo | Study on Airbag Concept for Motorcycles Using Opposing Vehicle as Reaction Structure[END_REF]. The direct exposure of a motorcyclist to the external environment and the absence of seatbelts are major challenges facing the installation of airbags [START_REF] Rogers | Factors and status of motorcycle airbag feasibility research[END_REF].

All the above passive safety systems can only help to minimize the effects of injuries; while, active safety systems can help avoid accidents. Among active safety systems, ABSs and ESC are less effective when the motorcycle is struck from behind by another vehicle. Additionally, both systems have already been fully developed and have been in use for some time and therefore cannot be greatly improved.

As a type of active safety system, collision warning systems can be effective in reducing the number of motorcycle accidents. These warning systems can be equally valuable for frontal and rear collisions. However, a limited work has been reported for motorcycle collision detection or warning system.

Related Work for Vehicle Detection and Classification

For car and other vehicle drivers, there are many types of collision warning system. These systems rely on global position system (GPS) based inter-vehicle communications method [START_REF] Ghatwai | Vehicle to vehicle communication for crash avoidance system[END_REF][START_REF] Zhang | A method for connected vehicle trajectory prediction and collision warning algorithm based on V2V communication[END_REF], radar [START_REF] Meinl | An experimental high performance radar system for highly automated driving[END_REF], laser-scanners [START_REF] Zhang | Efficient L-shape fitting for vehicle detection using laser scanners[END_REF], ultrasonic [START_REF] Jiménez | Vehicle tracking for an evasive manoeuvres assistant using low-cost ultrasonic sensors[END_REF] and camera-based approaches [START_REF] Kuo | Vision-based vehicle detection for a driver assistance system[END_REF][START_REF] Sivaraman | Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis[END_REF], etc., for collision detection as given in Table 2.2 below.

Table 2.2: Advantages and disadvantages of collision detection techniques.

Types of Collision Detection Technique

Advantages Disadvantages

GPS (Inter-vehicle communications).

Low cost and reliable.

Require two-way communications with another vehicle.

Radar. High performance.

Very expensive. Therefore, not suitable for motorcycle application.

Laser scanners. High performance.

Very expensive. Therefore, not suitable for motorcycle application.

Ultrasonic. High accuracy. Small range. Therefore, greater chances of collision. Camera-based approaches.

Low cost and high accuracy.

Less efficient in poor weather conditions, like fog or rain.

Every vehicle detection technique has some limitations. For example, GPS and inter-vehicle communication systems require two-way communication with another vehicle. That decreases the performance of the collision warning system relies on these vehicle detection techniques. Similarly, ultrasonic sensors have small range; therefore, the collision warning system based on it is less accurate than the camera and laser radar-based system. On the other hands, laser radar more is expensive than the camera-based system.

While, images and videos provide rich data sources, from which additional information and context can be surmised. Cameras provide a wide field of view, allowing for the detection and tracking of (moving) objects across multiple lanes. The cameras are less expensive as compare to radar and laser scanners. Therefore, the camera-based techniques can give reliable results to detect the rear end vehicles.

Moreover, audio microphones can be used for vehicle detection and classification. These sensors can even work during poor weather conditions and even at night time [START_REF] Toyoda | Traffic monitoring with ad-hoc microphone array[END_REF][START_REF] Shah | Classification of vehicles using adaptive neuro fuzzy inference system[END_REF]. They are cheap in comparison to other sensors, easy to install, with low power consumption and rather uncritical positioning demands. No direct line-of-sight is needed when analyzing acoustic signals, making acoustic vehicle detection and classification very attractive. Every vehicle while moving on the road makes some noise. This noise pattern helps to detect/classify the vehicle.

The vehicle classification by using visual information is very complex. Many types of vehicles belonging to the same class have features of various sizes and shapes. Moreover, occlusion, shadow, and illumination make the visual based classification task even more challenging. Also, it is highly unlikely to find the approaching vehicle location by using a single audio microphone. Therefore, the visual technique can be used for vehicle detection and audio technique can be used for vehicle classification.

Vision-based Vehicle Detection Techniques

In general, vehicle detections using cameras can be classified as stereo and monocular [START_REF] Sivaraman | Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis[END_REF]. Stereo-based methods require two images, leading to the increase in system complexities and costs. In contrast, monocular vision-based techniques used only one image to predict about potential collisions.

For a monocular based system, the determination of vehicle locations is performed by analyzing the vehicle's motion or appearance [START_REF] Sivaraman | Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis[END_REF]. In a motion based technique, an optical flow method is used to detect the vehicles [START_REF] Martinez | Driving assistance system based on the detection of head-on collisions[END_REF][START_REF] Alonso | Lane-change decision aid system based on motion-driven vehicle tracking[END_REF]. Motionbased methods are effective for detecting moving objects; however, they are computationally intensive and require analysis of several frames before an object can be detected. They are also sensitive to camera movement and may fail to detect objects with slow relative motion [START_REF] Teoh | Symmetry-based monocular vehicle detection system[END_REF]. As such, motion-based techniques are less commonly used for vehicle detections.

On the other hand, appearance-based detection techniques detect vehicles based on shadow underneath the vehicles [START_REF] Arenado | Monovision-based vehicle detection, distance and relative speed measurement in urban traffic[END_REF], color [START_REF] Tsai | Vehicle detection using normalized color and edge map[END_REF], symmetry [START_REF] Kuo | Vision-based vehicle detection for a driver assistance system[END_REF][START_REF] Teoh | Symmetry-based monocular vehicle detection system[END_REF], texture [START_REF] Kalinke | A texture-based object detection and an adaptive model-based classification[END_REF],

lights [START_REF] Wang | Driver assistance system for lane detection and vehicle recognition with night vision[END_REF], and edges [START_REF] Wong | Low relative speed moving vehicle detection using motion vectors and generic line features[END_REF], etc. In appearance-based techniques, color is a useful cue for segmenting the vehicle from the background. However, color-based object detection is very sensitive to illumination changes and the reflectance properties of the object. Similarly, the selection of the search window size for symmetry is critical; therefore, it can affect the vehicle detections. Also, the texture based vehicle detection generates a lot of false detections especially in an urban environment [START_REF] Teoh | Symmetry-based monocular vehicle detection system[END_REF].

Vehicle light pairs can only be used to detect the vehicles at night driving.

However, the vehicle rear or frontal views show strong vertical/horizontal edges and corners. These characteristics can be used to hypothesize the presence of vehicles.

Recent related studies for vehicle detections are given in Table 2.3 below. The table covers the detection type and the dataset properties; it also highlights some results through metrics such as accuracy and true positive rate, and it further provides the limitation of these techniques. In Table 2.3, the "Detection type" column defines whether the technique is applied either for the front or rear end vehicle detection. The "Dataset" column provides information related to the videos, the number of images or the type of vehicles involved in the experiments.

M. I. Arenado et al. [START_REF] Arenado | Monovision-based vehicle detection, distance and relative speed measurement in urban traffic[END_REF] used a single camera to detect the front vehicles. They used a combination of two features namely the shadow underneath the vehicle and horizontal edges for vehicle detection; the analysis of consecutive frames is used to calculate the relative speed of the detected car. Unavoidably, to detect the front vehicle license plate, both vehicles should be very close to each other, imposing a constraint that could be dangerous under a high-speed scenario. As reported, this technique is quite slow, as it processes only four frames per second.

A monocular vision based rear vehicle detection and tracking system for car drivers is presented in [START_REF] Liu | Rear vehicle detection and tracking for lane change assist[END_REF]. The camera was positioned looking backward out of the rear windshield. The application was for the detection of the front parts of the approaching vehicles to assist the driver in lane changing. Symmetry and edge operators were used to generate the region of interest. Subsequently, vehicles were detected using Haar wavelet features which were later fed to a support vector machine (SVM) classifier. The technique is only effective to detect the vehicles in an adjacent lane. Front.

1,000 images. 92.09 -------HOG/SVM. If the system fails to select the shadow area, the vehicle detection is performed using the HOG feature of the whole image; this slows down the detection process.

M. Men and F. Dai. (2015) [START_REF] Men | Multiple Features Fusion for Front-View Vehicle Detection[END_REF] Front.

1 video.

--------------HOG for Vehicle license plate localization and using lamp shape information.

In order to detect the front vehicle license plate, both vehicles should be very close to each other and this can be dangerous under the high-speed scenario.

M. R. O'Malley et al. [START_REF] O'malley | Vision-based detection and tracking of vehicles to the rear with perspective correction in low-light conditions[END_REF] proposed rear end vehicles detection under low light conditions. Their technique identifies the vehicle headlamp pairs using a region growing threshold and a cross-correlation bilateral symmetry analysis method. This technique performs a perspective transformation to correct the distortion and ensure consistent detection performance throughout all road maneuvers. Finally, a Kalman filter is used for tracking purposes. Unfortunately, this technique is only effective at night or when the light condition is very low.

C. Wu et al. [START_REF] Wu | Adjacent lane detection and lateral vehicle distance measurement using vision-based neuro-fuzzy approaches[END_REF] detected vehicles in adjacent lanes by placing a camera at the left side rear view mirror. The camera captures the images in the adjacent lane to detect vehicles. They use a neuro-fuzzy network to detect the vehicles. The training of the neuro-fuzzy network plays an important role in the detection process.

While in [START_REF] Lee | On-road vehicle detection based on appearance features for autonomous vehicles[END_REF] a monocular vision based technique has been proposed to detect front vehicles. Histograms of oriented gradients (HOG) have been used to extract the features, and SVM has been used for classification. Shadows underneath the vehicles have been used as a feature for vehicle detections. However, if the system fails to select the shadow area, the vehicle detection is then performed by using HOG features, which result in larger amounts of calculations and slower processing speed.

M. Men and F. Dai [START_REF] Men | Multiple Features Fusion for Front-View Vehicle Detection[END_REF] used the license plate to identify front vehicles. Their technique first detects the license plate of the front vehicle and verifies it by using the geometrical characteristics of the license plate. Afterwards, the rear lamps of the vehicle are detected across the license plate for the verification of the vehicle detection. However, in order to detect the front vehicle license plate, both vehicles should be close to each other, a situation that can be dangerous under a high-speed scenario. M. Kim et al. [START_REF] Kim | On road vehicle detection by learning hard samples and filtering false alarms from shadow features[END_REF] used the shadow underneath the vehicle to detect the frontal vehicles. Haar-like features with AdaBoost were used to train a shadow detector offline and SVM has been used for the classification.

Vision-based Vehicle Classification

Different techniques have been proposed for vision-based vehicle classification;

recent related work for vision-based vehicles classification is given in Table 2.4

below. Most of these techniques have been implemented for vehicle surveillance purpose. However, it is also suggested that these techniques can be used for collision warning system [START_REF] Hussain | On-road vehicle classification based on random neural network and bag-of-visual words[END_REF]. These techniques extracted different vehicle features from the image such as geometry [START_REF] Shantaiya | Multiple class image-based vehicle classification using soft computing algorithms[END_REF], appearance [START_REF] Moussa | Vehicle type classification with geometric and appearance attributes[END_REF] and texture [START_REF] Xiang | An Effective and Robust Multi-view Vehicle Classification Method Based on Local and Structural Features[END_REF], etc. ----

In [START_REF] Shantaiya | Multiple class image-based vehicle classification using soft computing algorithms[END_REF][START_REF] Moussa | Vehicle type classification with geometric and appearance attributes[END_REF] vehicles geometry and appearance are used to classify the vehicles into different categories. S. Shantaiya et al. [START_REF] Shantaiya | Multiple class image-based vehicle classification using soft computing algorithms[END_REF] classified the vehicles on the bases of its two or three-dimensional geometrical properties such as area, perimeter, major axis, minor axis, convex area and eccentricity, and solidity etc. An Artificial Neural Network (ANN) classifier with 10 layers is used for classification. However, the proposed technique only tested on 60 images and was limited to the self-recorded dataset. Also, vehicles are classified into two types (i.e. motorcycle and car) only.

Similarly, in [START_REF] Moussa | Vehicle type classification with geometric and appearance attributes[END_REF] This technique is only validated by the self-recorded dataset consists of 8053 images.

Also, it was limited to the surveillance application.

The vehicle detection and classification using images is still a challenging task. This is due to the similarity in vehicle appearance and a large number of vehicle types. Many types of vehicles belonging to the same class have features of various sizes and shapes. Moreover, occlusion, shadow, and illumination make the classification task even more challenging [START_REF] Hussain | On-road vehicle classification based on random neural network and bag-of-visual words[END_REF][START_REF] Abdulrahim | Traffic surveillance: A review of vision based vehicle detection, recognition and tracking[END_REF]. Variation of light, weather and day/night scenario makes this technique even more complex to detect the vehicle type from the images [START_REF] Abdulrahim | Traffic surveillance: A review of vision based vehicle detection, recognition and tracking[END_REF].

Audio-based Vehicle Detection and Classification

Acoustic sensors have many advantages in the context of vehicle detection classification, such as low cost, low power consumption, non-line of sight measurements, and independence of lighting conditions [START_REF] Toyoda | Traffic monitoring with ad-hoc microphone array[END_REF][START_REF] Shah | Classification of vehicles using adaptive neuro fuzzy inference system[END_REF]. Every vehicle that moves through the road makes some sounds. Their acoustic sounds are mostly influenced by the engine noise, exhaust noise, honk noise, tires noise and air turbulence; therefore, it can use to detect and classify the vehicles [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF].

Engine noise depends on vehicle cylinders and it varies with the speed of the vehicle. While an exhaust noise is directly proportional to the load of the vehicle.

Engine and exhaust noises can be useful to classify the type types of the vehicle. The honking noise has several harmonics around 2 kHz to 4 kHz. Tire noise is dominant among other vehicle acoustic sounds in a scenario when the vehicles are moving at the speed of 50 km/h or higher. It has two components named as vibrational noise (i.e., its energy spectrum lies between 100-1000 Hz) and air noise (i.e., its energy spectrum lies between 1000-3000 Hz) respectively [START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF][START_REF] Borkar | Review on vehicular speed, density estimation and classification using acoustic signal[END_REF]. Finally, an air turbulence noise generated by the boundary layers of the vehicles which cause the air flow. It immediately appears after the vehicle passes the audio microphone [START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF][START_REF] Borkar | Review on vehicular speed, density estimation and classification using acoustic signal[END_REF].

There are many techniques for audio-based vehicles detection and classification.

Most of these techniques are used for surveillance applications. Related studies for audio-based vehicle detection and classification are given in Table 2.5 below.

Vehicles are mostly classified on the bases of types [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Rahim | Heterogeneous multi-classifier for moving vehicle noise classification[END_REF][START_REF] Kubera | Audio-based hierarchic vehicle classification for intelligent transportation systems[END_REF][START_REF] Alexandre | Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications[END_REF][START_REF] Mayvan | Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors[END_REF][START_REF] Paulraj | Moving vehicle recognition and classification based on time domain approach[END_REF], manufacturers [START_REF] Shah | Classification of vehicles using adaptive neuro fuzzy inference system[END_REF][START_REF] Barai | Mechanical Condition Determination of Vehicle and Traffic Density Estimation Using Acoustic Signals[END_REF] and size [START_REF] George | Vehicle detection and classification from acoustic signal using ANN and KNN[END_REF], etc. The vehicle types which are mostly used for classification are bus, truck, van, cars, and motorcycles. For example, in [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF], vehicle are classified into seven classes named as 1) bus, 2) small truck, 3) big truck, 4) van, In [START_REF] Mayvan | Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors[END_REF][START_REF] Paulraj | Moving vehicle recognition and classification based on time domain approach[END_REF], vehicles are classified into four classes. In [START_REF] Mayvan | Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors[END_REF] The adaptive neuro-fuzzy inference is used for vehicle classification. Adaptive neurofuzzy inference system is the combination of artificial neural network and fuzzy logic system. The accuracy of the vehicle classification is not provided.

Vehicles classification is also performed by their weight or by the number of wheels. A total number of 160 vehicles are detected and classified into heavy, medium and light vehicles in [START_REF] George | Vehicle detection and classification from acoustic signal using ANN and KNN[END_REF]. Trucks and buses are classified as heavy vehicles;

while, rickshaws and two-wheelers are classified as light vehicles. Peak energy of the audio signal is used to detect and classify the vehicles. According to the authors, the horn signal has a peak around 3 to 5 kHz. For vehicle detection and classification, the horn peaks have been excluded from energy spectrum. Artificial Neural Network (ANN) performs better as compared to the K-nearest neighbor (KNN) classifier.

Similarly, M. Górski and J. Zarzycki [START_REF] Górski | Feature extraction in vehicle classification[END_REF], detected and classified vehicles into heavy tracked (HT), light tracked (LT), heavy wheeled (HW) and light wheeled (LW)

vehicles respectively. The audio data is recorded at the sampling frequency of 48 kHz.

The audio data is divided into small frames, with each frame consists of 256 audio samples. Afterwards, vehicle noise energy, Schur coefficients and Mel Frequency Cepstrum Coefficients (MFCC) are extracted from the audio data. The proposed technique achieves maximum accuracy of 84% by using Harmonic line features obtained from the vehicle noise energy.

In [START_REF] Bhave | Vehicle engine sound analysis applied to traffic congestion Estimation[END_REF], vehicles are classified into two, three wheelers and heavy vehicles. However, there are many challenges which still need to be addressed. For example, getting the exact location of the approaching vehicle by using a single microphone is not possible. Also, most of the above techniques are proposed for surveillance purpose where the microphones are installed at the fixed place. While, in our case, the proposed vehicle (i.e., motorcycle) is moving. It is very challenging to detect the approaching vehicle by using a single microphone. Also, it is important to minimize the effect of air turbulence for the collision warning system as it appears after the vehicle passes and can cause the false vehicle detection. Lastly, we have to avoid the acoustic sounds of the vehicles which are moving in opposite directions.

Audio-Visual based Vehicle Detection and Classification

Modest work has been reported in the field of audiovisual based vehicle detection and classification as given in Table 2.6 below.

In [START_REF] Smaldone | Improving Bicycle Safety through Automated Real-Time Vehicle Detection[END_REF] Similarly, in [START_REF] Piyush | Vehicle detection and classification using audio-visual cues[END_REF][START_REF] Daniel | Fusion of audio visual cues for vehicle classification[END_REF] However, the proposed technique achieves very low accuracy (66.00%) when it uses audio features only.

A. Klausne et al. [START_REF] Klausne | Vehicle classification on multi-sensor smart cameras using feature-and decision-fusion[END_REF] proposed an audio-visual based vehicle surveillance system. Genetic algorithm (GA) is used to reduce the features. A total number of 250 vehicle samples are used in the proposed method (i.e., 30% of the samples are used for training and other 70% samples are used for the testing). The vehicles are classified into three different classes named as 1) cars, 2) small trucks and 3) large trucks, respectively.

Directional Microphones

Microphones have many advantages as they are low cost, low power consumption, non-line of sight measurements, and independence of lighting conditions [START_REF] Toyoda | Traffic monitoring with ad-hoc microphone array[END_REF][START_REF] Shah | Classification of vehicles using adaptive neuro fuzzy inference system[END_REF]. Also, every vehicle that moves through the road makes some sounds. Their sound signature consists of engine, exhaust, honk, tires and air turbulence sounds, respectively [START_REF] Wieczorkowska | Spectral features for audio based vehicle and engine classification[END_REF][START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF]. Therefore, these signatures can be used to detect and classify the vehicles.

An audio-based vehicle detection and classification is very easy for surveillance application. However, in order to detect/classify the rear end vehicles for collision avoidance purpose, it is important to avoid the sound of the vehicle at which equipment is installed. Moreover, for accurate vehicle detection/classification, it is compulsory to avoid the vehicle sounds moving in opposite direction. A directional microphone can provide better recognition as compared to an omnidirectional microphone when multiple sources are present and are spatially dynamic [START_REF] Fabry | Adaptive directional microphone technology and hearing aids: Theoretical and clinical implications[END_REF].

Therefore, a directional microphone can be helpful to minimize the sound effects of the vehicle moving in opposite direction. The polar pattern of a directional microphone is given Figure 2.2 below.

Response of Drivers

To find the effectiveness of these collision warning systems, it is important to study the response of drivers against these systems. These studies can be divided into two categories: 1) studying the behavioral responses of drivers against these warning systems, and 2) Electroencephalography (EEG) based physiological response study of drivers.

Drivers Response Study based on the Behavior

Many researchers studied the behavioral responses of drivers against warning systems and found that these systems are effective and can assist the drivers to avoid the potential accidents. In these studies, many parameters such as drivers' reaction time, brake time and time to the collision, etc. are computed to find the alertness of drivers. Most of these studies are performed on simulators due to the pre-crash scenarios which are potentially dangerous to subjects [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF][START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF][START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF][START_REF] Scott | A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving[END_REF][START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF][START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF][START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF]. Some of the related studies are given in Table 2.7 below.

X. Yan et al. [START_REF] Yan | Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor[END_REF] analyzed the timing of early warnings, i.e. how far early warnings can be given? In this research, the collision warning system gives an early auditory warning at exactly three or five seconds, before the occurrence of potential collisions. The warnings, which were given three seconds before the occurrence of a potential collision, only alert the drivers about the incoming danger. On the other hand, in five seconds early auditory warnings, the directional information of the potential danger is also provided to the drivers. In this study, it has been observed that both auditory warnings can help to avoid the collisions. However, early warnings that are given before five seconds reduced further chances of vehicle collisions than that three-second early warning. Also, it has been observed that the directional information of a potential danger can be effective to avoid the accidents. Furthermore, in this research, a small variation in brake reaction time is observed for different age group drivers. Yet, these variations were found negligible.

J. Fagerlönn [START_REF] Fagerlönn | Distracting effects of auditory warnings on experienced drivers[END_REF] used two different audio warnings to alert the drivers about the pedestrians standing close to the roadside. Both auditory warnings started with a 1000 milliseconds verbal message, "pedestrians", followed by one of two sets of tone bursts that lasted for 1500 milliseconds. This research concluded that, although these warnings increase the responses of drivers, they can also cause the distraction for experienced drivers in demanding traffic situations. These warning messages may potentially impact drivers' responses to unpredictable events in the dense traffic scene.

52

The comparison of different types of collision warnings is also performed to find the effective types of warnings for drivers. For example, C. Ho et al. [START_REF] Ho | Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator[END_REF] presented the comparison of visual and vibratory warnings. To give the visual warnings, a screen is positioned in the center of the driving simulator console. One of the three colors is displayed as a warning to the driver. While vibratory display consists of two tactors. One of the tactors was positioned on the front, in the middle of the participant's stomach; while the other was positioned in the middle of the participant's back. By the comparison of both visual and vibratory warnings, it has been observed that the vibratory warnings further assist drivers to avoid potential danger.

auditory warnings. In this research, it has been found that tactile warnings decreased driver brake reaction time, as compared to visual and auditory warnings. Similarly, the authors of [START_REF] Mohebbi | Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[END_REF] also compare the audio and tactile warnings, while the driver was distracted using a phone. Also, in this research, tactile warnings were found to be more effective than auditory warnings. In [START_REF] Naujoks | Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance[END_REF], visual-auditory and visual warnings are given to the drivers to study their responses. The authors observe that the collision warning system can be helpful to avoid the accidents. However, the false alarms can lead to decreased compliance with and effectiveness of the system.

The responses of drivers, when they were distracted by the secondary task are analyzed for forward collision warning systems [START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF][START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF]. M. L. Aust et al. [START_REF] Aust | Effects of forward collision warning and repeated event exposure on emergency braking[END_REF] used both visual and auditory warnings; while, M. Bueno et al. [START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF] used only auditory warnings to alert the drivers. In both types of research, it has been observed that the collision warning system is only effective when drivers are either not distracted or performing less demanding tasks. M. Bueno et al. [START_REF] Bueno | Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task[END_REF], also observe that drivers' age difference, gender and driving experience do not have a much significant effect on the drivers' response for these collision warning systems. J. Kazazi et al. [START_REF] Kazazi | Accident prevention through visual warnings: how to design warnings in head-up display for older and younger drivers[END_REF] claims that both (i.e. young and old) aged drivers response rapidly for the less critical situation.

However, older drivers' responded rapidly in a critical situation when they get the visual stop warning.

In [START_REF] Lester | Pilot results on forward collision warning system effectiveness in older drivers[END_REF][START_REF] Son | The effect of age, gender and roadway environment on the acceptance and effectiveness of Advanced Driver Assistance Systems[END_REF], the effectiveness of collision warning system is analyzed for the drivers within different age groups. In [START_REF] Lester | Pilot results on forward collision warning system effectiveness in older drivers[END_REF], authors claim that a younger driver can adapt more rapidly to the collision warning system, especially towards pedestrian incursion. However, J. Son et al. [START_REF] Son | The effect of age, gender and roadway environment on the acceptance and effectiveness of Advanced Driver Assistance Systems[END_REF] concluded that the female and the younger drivers show the lowest acceptance, whereas the male and the late middle age drivers were more likely to accept these systems.

Most of the above mention techniques estimated the performance of the collision warning system based on the driver's response time. Most of these techniques used simulator due to the safety of drivers. According to the above literature review, these collision warning systems can increase the response of drivers and can assist the drivers to avoid potential danger. These systems mostly use visual, auditory, tactile or combination of these warnings. In these warning systems, tactile warnings are found more convenient as compared to any other types of warnings.

Auditory warnings have the advantage of being "gaze-free", meaning the speaker does not to be in front of the drivers likewise LCD display for the visual warnings. The listener can rapidly learn the auditory warnings. Also, these auditory warnings can convey spatial information regarding potential collisions, thus may reduce the perception and response time to the danger [START_REF] Meng | Tactile warning signals for in-vehicle systems[END_REF]. Moreover, the auditoryverbal warnings are found less annoyed as compared to the auditory buzzer warnings [START_REF] Politis | To beep or not to beep?: Comparing abstract versus language-based multimodal driver displays[END_REF].

These collision warning systems were found less effective, unusually when drivers were performing a secondary task. Some contradictions are found in the statements by researchers, especially for different aged drivers' response against different collision warning systems. Also, tactile warnings cannot use for Electroencephalography (EEG) studies, as movements or motion can affect EEG signals.

EEG based Drivers Physiological Response Study

Many researchers have studied the effectiveness of these systems by using Electroencephalography (EEG). From the literature review, it has been observed that these systems increased the drivers' response and can help to minimize accidents that may occur due to driver distraction or drowsiness. This section includes some of these states of art studies. This section is further divided into four subparts. First three sections explain about the EEG; while, the fourth section covers the EEG based effectiveness studies for collision warning systems. In most of EEG studies, usually, alpha, beta, theta and delta waves are used for sleep studies. Alpha and beta waves can be used to represent conscious states, while theta and delta waves are mostly used to represent unconscious states [START_REF] Sharma | Objective measures, sensors and computational techniques for stress recognition and classification: A survey[END_REF]. The frequency range for these EEG waves is given Table 2.8 below. Similarly, event-related potential (ERP) components can also be used to measure the driver response [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF].

Introduction to Electroencephalography and Event-Related Potential

Position of EEG Electrodes

Many researchers utilized the 10-20 international system used for the positioning of EEG electrodes because of its efficiency [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF]. The 10-20 international System describes the locations of EEG electrodes and is based on the area of the cerebral cortex. The terms "10" and "20" refer to the distance between two adjacent EEG electrodes; which can either be 10% or 20% of the total front-back/right-left skull distance. Each EEG electrode has a letter for the identification of the brain lobe and the number of its hemisphere location. For example, in Pz electrode, P refers to the parietal brain lobe and z (zero) refers to the electrode placed on the midline.

Types of EEG Electrodes

There are two types of electrodes commonly used for EEG signal extraction namely wet electrodes and dry electrodes [START_REF] Mathewson | High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes[END_REF].

Wet electrodes are often used for EEG measurements and are made of silver chloride (AgCl) [START_REF] Fazli | Enhanced performance by a hybrid NIRS-EEG brain computer interface[END_REF][START_REF] Koelstra | Deap: A database for emotion analysis; using physiological signals[END_REF]. These electrodes and skull tissue interface are resistive and capacitive. Therefore, these electrodes act as a low pass filter and block many EEG signals. To solve this issue, EEG gel is used. This gel creates a conductive path between the electrodes and skin by reducing the impedance to an acceptable value [START_REF] Mathewson | High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes[END_REF].

However, a continuous maintenance is required for these wet electrodes to achieve good quality EEG signals [START_REF] Nicolas-Alonso | Brain computer interfaces, a review[END_REF]. The preparation procedure for the use of these electrodes is very uncomfortable and time-consuming. The gel leakage can cause a short circuit between different electrodes. Also, the rapid use of gel can cause allergy or any other infection. Moreover, if the gel gets dry the quality of EEG signals degraded with it [START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF].

In order to address these limitations, many kinds of dry electrodes have been proposed [START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF][START_REF] Liu | Improving driver alertness through music selection using a mobile EEG to detect brainwaves[END_REF]. These electrodes do not use gel and are made of stainless steel and titanium. These dry electrodes can be made by the micro-electromechanical system (MEMS) technique.

MEMS dry electrodes acquire the EEG signals in an invasive way and are only limited to the forehead sites [START_REF] Lin | Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning[END_REF]. Furthermore, there are many drawbacks of these electrodes. First and foremost, a participant may feel the pain, when these electrodes penetrate the skin. Secondly, a proper physical strength is required for the penetration of these electrodes. Lastly, these electrodes have high manufacturing costs [START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF].

Other non-contact dry electrodes are also proposed for EEG signal measurements [START_REF] Mathewson | High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes[END_REF][START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF][START_REF] Liao | A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors[END_REF]. These non-contact dry electrodes can be made by using conductive polymer foam [START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF] or spring-loaded sensors [START_REF] Liao | A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors[END_REF]. These electrodes work perfectly even on hairy sites [START_REF] Mathewson | High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes[END_REF][START_REF] Lin | Novel dry polymer foam electrodes for long-term EEG measurement[END_REF][START_REF] Liao | A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors[END_REF]. For these electrodes, there is no need for gel or any skin penetration. Therefore, these non-contact dry electrodes can be used to get the EEG signals for the drowsiness detection of drivers.

EEG based Drivers Response Studies for Collision Warning Systems

Most related EEG studies to investigate the response of drivers are given in Table 2.9 below. The effectiveness of forward collision warning system by computing the different ERP components are examined in [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF]. In [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF], the participants were asked to follow the lead motorcycle. The participants were instructed to remove the feet from the acceleration pedal, as soon as they see the brake light of the motorcycle.

Also, auditory buzzer warnings are given to forewarn that the motorcycle is going to brake soon. Three warning conditions named as 1) no warning (i.e. no early warning is given about the motorcycle brake), 2) imperfect system (i.e. in this condition the early warnings were 70% reliable), and 3) perfect system (the warning conditions were 100% reliable) were presented to the participants.

The CNV is computed from 1000 milliseconds before the target to its onset; while, other ERP components are computed from 100-600 milliseconds after the target onset. The research concluded that the participants were quick in detecting the frontal motorcycle brake light when perfect and imperfect warning conditions were presented. Also, intentional cognitive levels were reduced for the perfect warning conditions. Significant difference observed at the neural level occurred within the N2 component latency range in the presence of collision warning system.

Likewise, M. Bueno et al. [START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF] followed the same driving protocols as given in reference [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF]. However, the participants were instructed to perform some tasks while driving. According to the authors, the latency of the P3 was reduced in the presence of the warning system compared to no system. Also, the presence of the warning system reduced the reaction time compared to no system at all.

To study the EEG response of participants, A. Fort et al. [START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF] presented the stimuli on a computer screen in the form of a rotating disc. The participants were instructed to remove the foot from the acceleration pedal as fast as possible in response to the bright red disc which constituted the visual target. In this study, three warning conditions likewise M. Bueno et al. study [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF] were presented to participants.

The experiment is divided into two sessions. During the first session only visual task is presented; whereas, in another session, the participant has to perform a secondary cognitive task. In this research, a clear amplitude reduction of the N1 component was observed when participants were engaged in the cognitive task. However, no difference was observed at the level of the sensory N1 component in distracted subjects. On the other hand, the latency of the N2 and P3 show a decrease. An amplitude of the P3 diminished in the dual task situation.

Z. Khaliliardali et al. [START_REF] Khaliliardali | Detection of anticipatory brain potentials during car driving[END_REF] used visual warnings to alert the subjects. A countdown appears at the center of the simulator screen from four to one, followed by a Go cue. Subjects were instructed to press the gas pedal as fast as they see the Go cue and drive the vehicle at the 100 km/h speed. After 15 seconds new countdown appears, followed by the stop cue. The subjects were instructed to immediately press the brake once they see the stop sign. Increase in CNV potential towards negative side is found for the Go/Stop cue. Thus, these visual warnings are found useful to warn against potential danger.

Researchers also analyzed the EEG based response of the drivers for the lane departure warning system using a car driving simulator [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF][START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF]. In [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF], the participants were instructed to drive the vehicle at the constant speed of 100 km/h.

After every 8 to 12 seconds, the vehicle randomly drifted towards the curb or into the opposite lane. The subjects were instructed to keep the straight line. If the subject reaction time was over the average reaction time for consecutive three trials, then the auditory warnings were given to alert the subject. The auditory warnings were given for 50% of these nonresponsive trials. The decrement in the theta and alpha power spectrum were found in the occipital region following the auditory feedback. Also, the reaction time of the trials with warning conditions was significantly shorter than those trials without warning condition.

In [START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF], the same driving protocol is followed as given in [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF]. However, C.-T. Lin et al. [START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF] used the auditory warnings for every trial when the subject reaction time was less (as compared to the average reaction time) for three successive trials.

EEG spectra following effective feedback differ significantly from those following ineffective feedback. Following an effective auditory feedback decrease in theta power band, alpha power band and reaction time have been observed. It shows that the feedback assisted subjects to reduce their drowsiness.

Also, Y.-T. Wang et al. [START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF] instructed the participants to drive in the third lane.

In this research, in addition to the lane change warnings, the drowsiness warnings are given to the participants. Drowsiness warnings are given when the alpha power is increased by 3 dB. These warnings are continued until the alpha power spectral reduces to the normal level. According to this research, auditory warning temporarily reduces the alpha and theta band power and mitigates the behavioral lapses. Also, the EEG spectrum changed back to normal within 10 seconds, after giving the auditory warnings.

In [START_REF] Huang | An EEG-based fatigue detection and mitigation system[END_REF][START_REF] Berka | Implementation of a closed-loop real-time EEG-based drowsiness detection system: Effects of feedback alarms on performance in a driving simulator[END_REF], the drivers are given the auditory warning, when they feel drowsy. K. C. Huang et al. [START_REF] Huang | An EEG-based fatigue detection and mitigation system[END_REF] also instructed the drivers to drive in their lane at the constant speed of 100 km/h and the vehicles were set to move randomly towards the left/right sides. The subject was instructed to move back the car into their lane. Driver reaction time and alpha waveband power were used to detect the driver drowsiness. The drivers were considered as drowsy when their EEG alpha power was greater than a certain threshold and drivers' reaction time was almost 2.5 times over the mean reaction time. For 50% of the drivers' drowsy states, the buzzer warnings were given.

EEG alpha and theta power decreased with auditory warnings and these warnings help the subjects to improve task performance. Also, in [START_REF] Berka | Implementation of a closed-loop real-time EEG-based drowsiness detection system: Effects of feedback alarms on performance in a driving simulator[END_REF] it has been found that auditory warnings increase the alertness of drivers.

There are many gaps in these studies. First, most of these studies focus only on the front collision warnings. However, EEG response studies can be performed for rear-end collision warning systems. These studies will help to analyze the decision making the power of drivers; either they need to increase the speed or change the lane.

Secondly, in these studies, the buzzer types of audio messages are given. Therefore, there is also a need to study the response of drivers against proper warning messages, which indicate the type of hazard.

Summary

Motorcycle accidents are contributing for more than 50% of the road fatalities in many Asian countries. To minimize the road fatalities different types of safety systems have been proposed. These safety systems can be divided into two categories named as 1) passive safety systems (which include helmets, protective clothes, and airbags etc.), and 2) active safety systems (which include ABS, ESC and collision warning systems).

Overall, the passive safety systems are found helpful to prevent the injuries and to minimize the consequence of the accidents; yet, these systems cannot assist to avoid the accidents. While active safety systems can help to avoid these accidents.

Among active safety systems, ABS and ESC are found less convenient to avoid rearend collisions. Collision warning systems work equally for frontal and rear-end collisions. These systems detect the potential hazards either it is from the front or rear end side and alerts the motorcyclists about it. However, little work has been reported

for motorcycle systems and it has many research gaps. Also, the implementation of many techniques has not been presented.

For the other vehicles, many types of collision warning systems are available.

These systems rely on GPS, camera, radar and laser scanner etc. GPS based systems cannot use, as their accuracy is very low. While the laser scanner and radar are very applications. However, extracting features for surveillance application is different as compared to the collision detection systems. In a surveillance application, air turbulence, the direction, and location of the vehicle did not affect the results. While, in collision detection system, air turbulence, the vehicles coming in opposite lane and the location of the vehicle at rear end can affect the results. Also, for the surveillance application the camera is fixed, therefore it is very easy to mark the region of interest in the image.

An audiovisual based vehicle detection study has also been found for the cycle application. However, this technique achieved very low accuracy for vehicle detection and also did not classify the vehicle into different categories, which can be more useful to avoid the accidents.

Many researchers have studied the response of drivers to analyzed the effectiveness of collision warning systems. These researchers mostly used drivers' reaction time, brake time and time to the collision, etc., to compute the drivers' reaction for these warning systems. Most of these studies are performed on simulators due to the pre-crash scenarios which can be dangerous for subjects. In these studies, it has been found that collision warning systems can alert the drivers in a dangerous situation and tactile warnings can be more convenient as compared to any other types of warnings. However, these warnings cannot be used for the EEG studies, as movements or motion can affect EEG signals. Therefore, audio and visual warnings are used to study the response of drivers.

EEG studies for the frontal collision warning system, lane change warning system and drowsiness warning systems have been performed to study the response of drivers. These EEG studies give more reliable results as compared to the other response studies and used the ERP and EEG power spectrum to compute the driver's response. In these studies, a decrease in EEG alpha and theta power has been observed when auditory warnings are given and also it has been noted that these warnings help the subjects to improve task performance. Also, the latency of the N1, N2, and P3 was reduced in the presence of the warning system. Furthermore, the presence of the warning system reduced the reaction time compared to no system at all. However, some gaps are found in EEG based collision detection studies. These studies mostly focus on the frontal collision warning systems and only used buzzer warning to alert the drivers.

Research Gaps

The research gaps in existing study can be divided into following three parts.

Limitations of Visual Techniques

In the existing vision-based vehicle detection methods, appearance-based techniques give more reliable results as compared to motion-based techniques.

However, motorcycles are smaller in size and have stronger vibration effects. These vibrations can affect the appearance features and lead towards false vehicle detections. Therefore, it is still challenging to find the prominent visual features which can be used for vision-based vehicle detection method for motorcycles. Also, audio and vision have different frame rate or sampling rate. Therefore, its integration with audio-based vehicle detection/classification method still needs to be studied.

Limitation of Acoustic Techniques

Moreover, every vehicle moving on a road generates different acoustic spectrum which can be used to detect its presence and to differentiate it from other vehicles.

However, the existing work has been limited to the surveillance application where microphones are fixed at some positions. While for collision detection, the microphone is moving with the source vehicle. Also, the acoustic spectrum consists of source vehicle and as well as approaching vehicles acoustic signatures. Therefore, it is very challenging to find the appropriate features from the acoustic spectrum, which can be used to detect and classify the approaching vehicles, even in the presence of multiple vehicles acoustic signatures.

Limitations of Drivers Physiological Response Studies

Previous physiological based response studies reported in the literature use the buzzer warnings only for the collision warning system and were limited to the frontal collisions for the car drivers. These studies investigated the latency and amplitude of ERP components N1, N2, and P3, respectively. The response of motorcyclists can be different as they are directly exposed to the external environment and their ride is smaller in size as compared to the car and other vehicles. Also, the motorcycle is a two-wheeler ride; therefore, it can easily get unbalanced. Therefore, it is important to investigate the EEG based response of drivers for the rear collision warning system using auditory verbal warnings. Other than N1, N2, and P300, it is important to investigate N400 ERP component as it represents the brain responses to verbal words.

50

CHAPTER 3

METHODOLOGY

In this chapter, a rear end collision detection and classification method for motorcyclists is presented. It also discusses the methodology for investigating the response of motorcyclists against the collision warning system. Two approaches (i.e., vision-based and acoustic-based techniques) have been used for collision detection and an acoustic method is used for vehicle classification. Finally, these techniques are merged together to design a safety system for motorcycles. To study the response of the motorcyclists N1, N2, P3, and N400 ERP components have been extracted from the EEG data.

Proposed Methodology

The proposed method is given in Figure 3.1 below. The methodology is divided into two parts. In part 1 (section 3.2) audio-visual based collision warning system is presented. Vehicles are detected by using the audio spectrogram and visual appearance features. While for vehicle classification, it only relies on the acoustic signatures. All these techniques are fused together by using the logical table.

Also, the physiological response of motorcyclists has been investigated for the rear end collision warning system (given in section 3.3). To investigate the response, auditory verbal warnings are used to alert about the approaching vehicles and the motorcyclists EEG data have been collected. Afterwards, ERP components N1, N2, P3, and N400 have been extracted to analyze the motorcyclist behavior.

Proposed Vehicle Detection and Classification Technique

We used visual/audio techniques for vehicle detection and then an audio technique for vehicle classification. Finally, both techniques merge together to design a rear-end vehicle collision detection system for motorcyclists.

Dataset

To validate the proposed methodology self-recorded as well as different online datasets are used. The details of all these datasets are given below subsections.

Self-Recorded Dataset

For vehicle detection and classification, the data have been collected by placing the Sony action cam and At-2035 cardioids condenser microphone at the rear end of the motorcycle, as shown in Figure 3.2 below. The details of the multimedia datasets for vehicle detection and classification are given in Table 3.1 below. The dataset was recorded in different road scenarios having various traffics and lighting conditions. The dataset has been collected at Ipoh-Lumut highway, Perak, Malaysia and inside Universiti Teknologi PETRONAS (UTP), Malaysia.

Visual information from the self-recorded dataset is used for vision-based vehicle detection and used to validate the audio results. While, an audio information is used to detect and classify the approaching vehicles, at the rear end of the motorcycle.

For audio-based vehicle classification, the dataset is classified into six categories named as 1) car, 2) motorcycle, 3) bus and 4) truck, 5) multiple vehicles and 6) no vehicle, respectively. When two or more types of vehicles approached towards the motorcyclist from the rear end, these vehicles are categorized as multiple vehicles.

Also, when there was no vehicle at the rear of the motorcycle the audio signature placed in the no vehicle category.

Online Visual Dataset

Due to the limitations of online motorcycle datasets, the proposed vision-based vehicle detection technique has also been validated on online car datasets [START_REF] Sivaraman | A general active-learning framework for on-road vehicle recognition and tracking[END_REF][START_REF] Rezaei | Vehicle detection based on multi-feature clues and Dempster-Shafer fusion theory[END_REF][START_REF] Choi | Realtime on-road vehicle detection with optical flows and Haar-like feature detectors[END_REF].

The details of the online vision-based vehicle detection datasets are given in Table 3.2 below.

Online Audio Dataset

There was no such online dataset for audio-based vehicle classification; therefore, for the comparison purposes, those datasets which contains some vehicle sounds are used. Two online datasets namely ESC-50 [START_REF] Piczak | ESC: Dataset for environmental sound classification[END_REF] and LITIS Rouen [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] are used to validate our technique; the details are given in Table 3.3 below.

Visual Technique for Vehicle Detection

The steps leading to the algorithm development are illustrated in Figure 3.3 below. To get the ROI, a fixed area from the top of every grayscale frame is excluded to remove the sky and other unwanted regions. Next, the left and right boundaries of the ROI image are kept the same as the input frame. After that, binary road region segmentation and vehicle detections are performed; both approaches will be explained in the next subparts.

Vehicle detection

To find the vehicle pattern initially, Sobel edge detection [START_REF] Gonzales | Digital Image Processing[END_REF] with Otsu threshold [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF] has been applied to the ROI image. The Sobel operator is a discrete differential operator. The primary advantages of the Sobel operator lie in its simplicity. Apart from the edges detection, it can also detect their orientations.

The resultant image consists of the lane markers and vehicle footprints as shown in Figure 3.8 below. Hough transform can be used to isolate the features of a particular shape in an image. Therefore, it is used to detect the angular lines, which further helps in detecting the vehicle footprints and removing the shadow regions. Assume that h, mh and ah are the length, slope and the angle of the angular line, respectively, obtained from Hough transform.

If (x1, y1) and (x2, y2) are respectively the start and the endpoints of the angular line, then the length h, slope mh, and angle ah are calculated as:

) 1 2 ( 2 ) 1 2 ( 2 y y x x h - + - = (3.4) 1 2 1 2 x x y y h m - - = (3.5) ) ( tan 1 h m h a - = (3.6)
In our technique, the angular range selected for these lines is given as: For the vehicle footprint validation, a comparison of the selected lines and Harris corner detection is performed. For this comparison, initially, an image is divided into small patches. In all patches, the slopes of the selected lines are computed. For example, in any patch, if (xp, yp) and (xq, yq) are the starting and the end points of the selected line, respectively, then the slope mh of this line is calculated as: For the proposed technique, the size of six selected patches for ROI, their locations and the values of other parameters such as M(i), ah and h are kept the same for all types of road scenarios and light conditions. The same values are used for the self-recorded and online datasets. Each dataset has different frame sizes and a different placement of the camera; therefore, the fixed area which has been excluded from the top of every grayscale frame varies for each dataset.

   
p x q x p y q y h m - - = (3.
Lastly, the binary road image is used to make a decision whether the selected lines represent a vehicle or not. Indeed, the boundaries of the road are clearly black, which enables us to mark the road edges and allows us to discard the lines appearing outside of these marked edges. The remaining lines contain the footprint of the vehicle.

Audio-based Vehicle Detection

The proposed methodology for audio-based vehicle detection is shown in Figure 3.12 below. The details of each part are given in the subsequent subsections.

Filtration Operation

At first, a high pass finite impulse response (FIR) filter with cutoff frequency of 100 Hz has been applied to the audio data for minimizing the air turbulence effects.

FIR Filters are inherently stable. They are easy and convenient to implement. For example, if x[n] is the input signal, then the output f [n] of the FIR filter is given as:

] [ ] [ ] [ 1 0 r n x r h n f f N r - =  - = (3.12)
Where Nf and h[n] are the filter order and the impulse response of the FIR filter, respectively. If FC is the cutoff frequency, then the response h[n] of high pass FIR filter is given below.

     = -  - - - = 2 / 2 1 2 / ) ( )] ( 2 sin[ ] [ 2 2 f C f f N f N C N n F N n n n F n h   (3.13)
The value of Nf is 50; therefore, the impulse response of the FIR filter is given as:

     = -  - - - = 25 2 1 25 ) 25 ( )] 25 ( 2 sin[ ] [ n F n n n F n h C C   (3.14)

Feature Extraction

Initially, the audio data has been divided into small frames. The size of the audio frame is adjusted in such way that the time duration of one audio frame is equal to the time duration of one video frame. For example, P is the number of audio samples in one audio frame and is given as:

r f V L P 1 = (3.15)
Where Lf 1 is the total number of audio samples in one second and Vr is the video frame rate.

To extract the features, Short-time Fourier Transform (STFT) has been applied to these audio frames. STFT enables to get the time-frequency distribution (TFD), which offers us the varying trend of a local frequency with respect to the time. For example, if f [n] is the audio signal obtained after the filtration process, then the output of the STFT is given as: ))

1 2 cos( 1 {( 5 . 0 ) ( - - = N n n w  (3.17)
Where N represents the length of the window and 0 ≤ n ≤ N.

The size of the sliding window has been selected equal to two audio frames with 50% overlapping. Let P be the total number of samples in one audio frame, then the Hann the window is given as:

Balancing Features Vector Length

The audio-based vehicle detection dataset was imbalanced. The number of samples when there were no vehicles on the road was greater than the number of audio samples when there were vehicles on the road. To solve this issue, two feature vector balancing techniques have been used: 1) undersampling and 2) oversampling.

In undersampling, the length of majority class has been reduced by selecting some samples. For example, if n is the number of samples in our minority class (i.e., vehicles samples), then the same number of samples is selected from the majority class (i.e., no vehicles samples) by using an undersampling technique. The following undersampling techniques have been used to balance the self-recorded vehicle detection dataset [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF]:

• Random under Sampling (RUS)

• Cluster Centroids (CC)

• Condensed Nearest Neighbour (CN)

• Edited Nearest Neighbours (ENN)

• Instance Hardness Threshold (IHT)

• Near Miss-1 (NM-1)

• Near Miss-2 (NM-2)
• Near Miss-3 (NM-3)

• Neighbour-hood Cleaning Rule (NCR)

• One-Sided Selection (OSS)

• Tomek Link (TL)

In RUS technique, the random numbers of samples are selected from the majority class and these samples in total are equal to the total number of samples in the minority class. Cluster centroids (CC) technique replaces samples of the majority class by the centroid values. CC method first computes the average value of each cluster within itself and then it calculates the samples (which belong to that cluster) distance from the average point. The numbers of clusters are decided by the level of undersampling [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF]. Condensed Nearest Neighbour (CN) was introduced by the scientist P.E. Har in 1968 to determine a consistent subset of the original sample set.

According to CN technique, every set is trivially a consistent subset of itself [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

ENN technique applies the nearest-neighbor algorithm and removes the samples which do not agree "enough" with their neighborhood. For each sample in the class to be under-sampled, the nearest-neighbors are computed and if the selection criterion is not fulfilled, the sample is removed. Two selection criteria are used in this technique given as 1) the majority or 2) all the nearest-neighbors have to belong to the same class than the sample inspected to keep it in the dataset. NCR used the union of samples of majority class to be rejected between the ENN and the output of a 3 nearest neighbors' classifier [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

In IHT technique, the classifier is trained on the data and the samples with lower probabilities of the majority class are removed. Also, three near-miss techniques have been used to balance our dataset. NM-1 selects the samples from the majority class whose average distances from three minority class samples are the smallest. NM-2 selects the samples from the majority class based on their average distances to three farthest minority class samples. NM-3 is a 2-steps algorithm. First, for each sample from minority class, their M nearest-neighbors are kept. Then, the majority class samples are selected whose average distance to the N nearest-neighbors is the largest [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

Tomek link used the nearest-neighbor (NN) rule to reduce the size of the majority class. It eliminates the certain samples (with known membership) of the majority class without affecting significantly the performance of NN classification. Also, OSS used the TL to remove noisy samples. In addition, the 1-nearest neighbor rule is applied to all samples and the ones which are misclassified are used in the majority class [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

In oversampling techniques, the samples of the minority class (i.e., vehicles samples) have been increased by using the re-sampling technique. The following oversampling techniques have been applied to the dataset [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

• Adaptive Synthetic (ADASYN)

• Random Over Sampling (ROS)

• Synthetic Minority Oversampling Technique (SMOTE)

• Borderline SMOTE of type 1 (SMOTE-B1) • Borderline SMOTE of types 2 (SMOTE-B2)
ROS technique duplicates some of the original samples of the minority class.

SMOTE and ADASYN generate new samples for minority class by interpolation.

ADASYN focuses on generating samples next to the original samples which are wrongly classified using a KNN classifier while SMOTE did not make any distinction between easy and hard samples to be classified using the nearest neighbors rule [START_REF] Lemaitre | Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[END_REF].

Vehicle Detection

For vehicle detection, singular value decomposition (SVD) technique has been applied to reduce the features. For example, if B is the feature matrix obtained from the data balancing technique, then it can be represented as:

T UDV B = (3.20)
where, U, D, and V represent the left singular vectors, singular values, and right singular vectors, respectively. A total of 98% energy is retained in D. These D matrix values are used to extract the new feature matrix from the left singular vectors (i.e., U matrix). The total number of new features obtained by using SVD for different data balancing techniques is given in Table 3.4 below. The size of new features matrix is 80% lesser as compared to the original feature matrix. After applying the SVD, the number of features for oversampling techniques were less as compared to the undersampling data balancing techniques. In oversampling, the feature matrices lengths were increased by adding more data from the same class.

Support vector machine (SVM) [START_REF] Cortes | Support-vector networks[END_REF] and random forest (RF) [START_REF] Breiman | Random forests[END_REF] classifiers have been used for the classification. Apart from recent artificial intelligence techniques employing CNN trained on large image dataset, SVM and RF have both been used in most of the supervised classification approaches outperforming the other classification methods.

A linear kernel has been used for the SVM to classify the extracted features into vehicles and non-vehicles categories. Two numbers of trees have been used for random forest classifier. These numbers of trees are greater than the square root of the total number of features.

Audio-based Vehicle Classification

The flow chart of the proposed technique is given in Figure 3.14 below. Two types of datasets are used: 1) self-recorded dataset and 2) online dataset are used for validation. The online datasets were recorded by using an omnidirectional microphone; while, our dataset was recorded by using a directional microphone. The detailed methodology for vehicle classification is given in the subsections below.

Filtration Operation

Initially, a high pass FIR filter with cutoff frequency of 100 Hz has been applied on the audio data to minimize the air turbulence effect and other unwanted noises. Let

x[n] is the input signal, then the filter signal f [n] is obtained by using equation 3.12.

The FIR filter has a linear phase response and has a cutoff frequency of 100 Hz; its response is given in equation 3.14.

Feature Extraction and Classification

For feature extraction, the audio signal is broken down into lower resolution components by using discrete wavelet transform (DWT). Wavelet decomposition provides us the detail coefficients and approximate coefficients. Let Di[n] and Ai[n] be the detail and approximate coefficients, for any decomposition level l are given as:

] 2 [ ] [ ] [ 1 r n g r A n A r l l - =    - = - (3.21) ] 2 [ ] [ ] [ 1 r n h r A n D w r l l - =    - = - (3.22)
where g [n] and hw [n] are the low pass filter and high pass filter, respectively.

Note that both approximation and detail wavelet coefficients at level l depend on the previous level (l -1) approximation wavelet coefficient. For first level (i.e., l = 1) the filter signal (i.e., f [n]) is considered as previous approximation coefficient.

] [ ] [ 0 n f n A = (3.23)
DWT allocating with Symlet 4 is applied to decompose an audio signal into one approximate and five detailed levels (l=5). Let Wi be the subband obtained by using DWT (using equation 3.21 and equation 3.22); that is,

  L i w w w W  2 1 = (3.24)
Where i = 1,2,3,....,6 and L is the length of the subband. W1, W2, W3, W4 and W5 refer to the detailed coefficients; while, W6 denote the approximate coefficient as presented in Table 3.5.

Table 3.5: Frequency band of the audio signal using 5 th level of decomposition.

Decomposition level

Sub Bands

Frequency Range (Hz) The percentage of energy (named as energy signal) has been computed for each subband. Let Ei be the percentage of the energy of the i th subband (where 1≤ i ≤ 6) is given as:

  L i e e e E  2 1 = (3.25)
Subsequently, the energy matrix has been created for each subband. For energy matrix creation, energy signal has been divided into small frames. The size of the audio frame is adjusted in such way, that the time duration of one energy signal frame is equal to the time duration of one video frame (equation 3.15).

Let P is the total number of energy values in one frame, K is the total number frame in the matrix then Ei can also be expressed as:

(3.26)
The matrix creation for Ei is given as:

(3.27)
The numbers of features in these frames are considered as energy values. Ei can be written as:

i P i R E  (3.28)
The number of features obtained for these matrices is given in Table 3.6 below. Figures 3.16 and 3.17 below show the energy matrices plots for the selfrecorded and for online datasets.

Also, the signal has been reconstructed for every subband. To extract the features, STFT has been applied for every sub-band by using equation 3.16. The Hann window is chosen as the sliding window with 50% overlapping. For example, if P is the total number of samples in one audio frame, then the Hann window is computed by using equation 3.18.

In order to find the vehicle footprints, a power spectrum has been calculated by squaring the magnitude of the STFT signal (equation 3. [START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF]). The output of the power spectrum is the two-dimensional matrix.

Let Si be the spectrum of the i th subband (where 1≤ i ≤ 6) obtained from the reconstructed signal of Wi, is given as: The number of features J obtained from the power spectrum can be expressed as:

            = KJ K K J J i s s s s s s s s s S       
i J i R S  (3.29)
The total numbers of power spectrum features are equal in all subbands. For self-recorded and ESC-50 [START_REF] Piczak | ESC: Dataset for environmental sound classification[END_REF] datasets the value of J is 1765 and equation 3. To find the vehicle footprints, both matrices are concatenated together, and the resultant matrix is known as a feature matrix. Let Fi be the feature matrix (obtained by the concatenation of Ei and Si) for the i th subband and is given as: The audio-based vehicle classification has been performed for feature matrix F1, F2, F3, F4, F5 and F6, respectively. Also, the feature matrix F7 has been computed by concatenating the features of F1, F2, F4, F5, and F6. Feature matrix F7 is given as:

i J i P R S E S E F i i i i i  = = ) , ( (3.32)
6 6 5 5 4 4 2 2 1 1 6 5 4 2 1 7 ) , , , , ( J P
J P J P J P J P R F F F F F F  = (3.35)
The number of features obtained for different datasets is given in Table 3.7. Subsequently, SVD has been applied to reduce the number of features. The SVD has been computed by using equation 3.20 and the total numbers of features obtained for the different dataset are in Table 3.8 below. Finally, the random forest classifier [START_REF] Breiman | Random forests[END_REF] with a total number of 50 trees, used for the classification. The fusion of these techniques not only gives the information about the approaching danger to the motorcyclist, it also provides the type of the incoming hazard.

Combination of Audio and Visual Techniques

When any vehicle gets close to the motorcycle from the rear end, the logical approach to detect the vehicle for the first time (i.e., the first frame detection of the approaching vehicle) is given in Table 3.9 below. Table 3.9: First frame analysis method for the combination of vehicle detection and classification techniques.

Vision based vehicle detection (V)

Audio based vehicle detection (A1)

Audio based vehicle classification (A2)

Result

0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 1* 1 0 1 1 1 1 0 1* 1 1 1 1
* Vehicle detection warning only.

The output of the audio-based vehicle detection and the audio-based vehicle classification techniques are denoted A1 and A2, respectively. Similarly, V is used to represent the output of the vision-based vehicle detection technique.

When any Q th vehicle gets close to the motorcyclist from the rear end, then the visual information (i.e., vision-based vehicle detection) is given a preference as compared to the audio information (i.e., audio-based vehicle detection and classification). This is due to the visual information-based vehicle false detection rate is less as compared to the audio-based techniques (as given in chapter 4).

In the fusion method, if any vehicle type is found first (by the audio-based vehicle classification technique) and the visual detection technique did not detect the vehicle yet; then, the audio-based vehicle detection and classification results are ignored, and the audiovisual technique output is considered as zero. On the other hand, if the vehicle is detected first by using visual information and the audio information did not give any result, then the motorcyclist will get the alert about the rear end vehicle. However, this alert will only consist of the vehicle detection.

To derive the Karnaugh map (K-map) equations for the first frame of any Q th vehicle the logical approach given in Table 3.10 below.

V (0) 0 0 0 0 V (1) 1* 1 1 1*
* Vehicle detection warning only.

For any Q th vehicle the output equation for vehicle detection (for the first frame) by the K-map is given as:

V Output = (3.36)
The output of any Q th vehicle for both vehicle detection and classification is given:

2 VA Output = (3.37)
After the first frame detection of any vehicle, a different approach is used to detect the next frame of that vehicle, as given in Table 3.11 below. Table 3.11: Combination of vehicle detection and classification techniques for any frame (i.e., other than the first frame).

Vision based vehicle detection (V)

Audio based vehicle detection (A1)

Audio based vehicle classification (A2)

Result

0 0 0 0 0 0 1 1 0 1 0 1* 0 1 1 1 1 0 0 1* 1 0 1 1 1 1 0 1* 1 1 1 1
* Vehicle detection warning only.

For any next frame (i.e., other than the first frame) of that vehicle, the consecutive frame analysis method is used to detect and classify it. For example, if the audio information gives some results about the vehicle type and the visual detection technique did not give any output. In such scenario, the visual results (i.e., the visionbased vehicle detection) from the previous frame are considered and the information about the approaching vehicle and its type will be given to the motorcyclist.

However, if the visual detection technique or audio-based vehicle detection

technique gives some results and the classification technique did not give any output, then only the vehicle detection output is considered only.

The logical approach to derive the K-map equation for any frame (other than the first frame) of the Q th vehicle given in Table 3.12 below.

V (0) 0 1 1 1* V (1) 1* 1 1 1* 85
The K-map equation for vehicle detection for any frame (other than first) of the N th vehicle is given as:

2 1 A A V Output + + = (3.38)
Similarly, the K-map equation for vehicle classification for any frame (other than first) of Q th vehicle is given as: From equation 3.39, the audio-based vehicle detection contributes for vehicle detection for consecutive frame analysis (i.e., other than the first frame).

Auditory Alerts for the Fusion Technique

To generate the auditory-verbal warnings, a window has been inserted in the visual image near the motorcycle. The size and the location of the window are given in Figure 3.20 below. The area inside the window is considered as a danger zone. The vehicles outside of this danger zone are also detected and classified. However, warnings are only generated for vehicles which appeared in that window. The first warning is originated when the vehicle appears in that window. Next to it, after each second the presence or not of a vehicle in the designated area is checked. In case of presence, a warning is again originated. The aim of this window is to minimize the auditory alerts about the approaching vehicle and concerns only potential hazard vehicles.

The auditory-verbal warning consists of two words. The first word is "Alert" and it is common in all warnings. The aim of this word is to get the attention of motorcyclist. The second word consists of a vehicle type, i.e., "motorcycle", "car", "bus", "truck", etc.

Hardware

The details of the hardware used for the vehicle detection and classification are given below.

Sony Action Cam AS100V

A Sony action cam AS100V has been used to record the visual dataset. The Sony action cam AS100V is small in size and can easily be placed at the rear end of the motorcycle. Due to its small size, it can easily fit in a shockproof cover to minimize the vibration effect on the video during recording.

The AS100V can also support different frame rate and image resolution. Also, it has built-in Wi-Fi and a GPS device. The GPS device stores the position of the motorcycle during the recording. It can be synchronized with the other devices by using, the date and time option.

At-2035 Cardioids Condenser Microphone

At-2035 cardioids condenser microphone is installed at the rear end of the motorcycle to record the incoming vehicle sounds. It can handle high-pressure sounds and can be used with phantom power, which makes it suitable for outdoor use. It can also support a cardioid polar pattern (also known as a directional pattern). The directional pattern of the At-2035 microphone is shown in Figure 3.21 below.

A directional pattern can be helpful to minimize the motorcycle engine sound and other sounds coming from the front side of the motorcycle (i.e. opposite to the At-2035 microphone).

Portable Recorder Roland R-26

At-2035 cardioids condenser microphone is used with the Roland R-26 portable sound recorder. Roland R-26 can be used with the phantom power. It has two built-in microphones; however, it also supports two external microphones. It can record different audio file formats such as .wav, .mp3 and .bwf, etc. The audio sampling rate can also be adjusted in the R-26 recorder. Date and time option in the R-26 recorder can be helpful to synchronize it with the Sony action cam.

EEG based Motorcyclist Physiological Response Analysis

The proposed collision warning system provides information about approaching vehicles. It also provides information about the hazard type. Motorcycles have different maneuverability as compared to cars and other four-wheeler vehicles. Furthermore, motorcyclists are directly exposed to the external environment. The motorcyclists may not react properly on the information provided about the potential hazard, during the driving session. Therefore, it is important to investigate their responses for the collision system using auditory verbal warnings.

The study of the motorcyclists' responses includes various stages such as, sample size calculation, subject recruitment, selection criteria, equipment used for data collection, data collection and data analysis which are explained in the following subsections.

Sample Size Calculation

For the sample size calculation, PS software is used with paired T-test [START_REF] Ps | Power and Sample Size Calculation[END_REF].

Previous studies have been used for statistical calculation of the sample size [START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF]. We planned a study of a continuous response variable from matched pairs of study subjects. Prior data indicate that the difference in the response of matched pairs is normally distributed with standard deviation 59. If the true difference in the mean response of matched pairs is 41, we will need to study 29 pairs of subjects to be able to reject the null hypothesis that this response difference is zero with probability (power) 0.95. The Type I error probability associated with this test of this null hypothesis is 0.05.

The sample size with α = 0.05 for different power size is given in Table 3.13 below. A sample size of 29 participants is selected to achieve significant results.

Participants Selection

For subject recruitments, the experiment was advertised through the public communication network (e.g., Facebook, Twitter, etc.). A lifestyle appraisal questionnaire was used as selection criteria, which required participants to have no medical contraindications such as severe concomitant disease, alcoholism, drug abuse, and psychological or intellectual problems more likely to limit compliance [START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF][START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF][START_REF] Huang | An EEG-based fatigue detection and mitigation system[END_REF][START_REF] Berka | Implementation of a closed-loop real-time EEG-based drowsiness detection system: Effects of feedback alarms on performance in a driving simulator[END_REF][START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF][START_REF] Yale | Neurologic conditions: assessing medical fitness to drive[END_REF][START_REF] Ma | A pilot study of fatigue on motorcycle day trips[END_REF][START_REF] Craig | The lifestyle appraisal questionnaire: a comprehensive assessment of health and stress[END_REF]. The subject selection criteria are given in Table 3.14 below.

Any subject with the following medical conditions was not included in the research.

A written consent form was obtained from each subject and all of them were financially compensated. The research protocol was approved by the Ethical Committee of the Department of Electrical and Electronic Engineering, Universiti

Teknologi PETRONAS (UTP), Malaysia. The format of the consent form and approval of the ethical committee are given in Appendix C and D respectively.

Apparatus for EEG Data Recording

The experiment was performed by using the motorcycle driving simulator located at the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti

Teknologi PETRONAS (UTP), Malaysia. A motorcycle simulator was used due to the safety of the motorcyclist, as the experiment contained some pre-crash scenarios [START_REF] Meng | Dynamic vibrotactile signals for forward collision avoidance warning systems[END_REF][START_REF] Meng | Tactile warning signals for in-vehicle systems[END_REF][START_REF] Politis | To beep or not to beep?: Comparing abstract versus language-based multimodal driver displays[END_REF][START_REF] Lin | Tonic and phasic EEG and behavioral changes induced by arousing feedback[END_REF][START_REF] Lin | Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra[END_REF][START_REF] Wang | Developing an EEG-based on-line closed-loop lapse detection and mitigation system[END_REF][START_REF] Huang | An EEG-based fatigue detection and mitigation system[END_REF][START_REF] Berka | Implementation of a closed-loop real-time EEG-based drowsiness detection system: Effects of feedback alarms on performance in a driving simulator[END_REF]. The motorcycle simulator is shown in Figure 3.22 below.

Stimuli and Procedure

Before the start of the experiment, the participants were briefly informed about the experimental procedure. All participants were given 10 to 15 minutes to become familiar with the motorcycle simulator, which they have to drive in the experiment.

After that, the EEG electrodes were installed, and the experiment began. They were instructed to keep the maximum speed of 90 km per hour. No foggy condition has been selected from the option to reduce the saccadic movements.

In the driving scenario, different types of vehicles approach the motorcycle from the rear end randomly after 6 to 12 seconds (mean 10 seconds). The participants were instructed to increase the speed or change the lane in order to avoid the rear end collisions. An auditory warning could forewarn the motorcyclists that they have the possible rear-end collision chances. The warning was presented randomly ranging from 1,500 to 2,400 milliseconds (i.e. mean 2,000 milliseconds) before the target simulating a rear end collision scenario.

Two types of warnings (i.e., buzzer sound and verbal sound) were presented to the motorcyclist about the potential hazard. The buzzer sound warning was 500 milliseconds long and consists of five pulses, at 2,000 Hz frequency for 80 milliseconds for each one, and with a shorter pause of 20 milliseconds between them.

The verbal warning was 1,200 milliseconds long and consists of the two auditory words (i.e., the first word is "Alert" and the other word consists of the approaching vehicle type; for example, "Car"). The first word is common in all instructions and it was used to get the attention of the motorcyclist. The second word is different for each warning; it is based on the approaching vehicle type (i.e., car, motorcycle truck, etc.).

In this research, the motorcyclist was given five warnings about the potential hazard of the approaching vehicles (from the rear end) as given in Table 3.15 below. Four warning conditions specifically no sound system (NSS), an imperfect buzzer system (IBS), an imperfect verbal system (IVS) and a perfect verbal system (PVS), were presented to the motorcyclists in separate blocks. The experimental design for all the four warning conditions is given in the Figure 3.24 below For NSS and PVS, 120 trials were presented in each warning condition. The total trials were further divided into four blocks of 30 trials and each block lasted around 6 minutes. For IBS and IVS, a total of 170 trials were presented in each warning condition. In this case, the trials were divided into five blocks of 34 trials and each block lasted for 6.8 minutes. The experiment was conducted in one session that lasted approximately two and half hours.

In the NSS condition, no warnings whatsoever were given before any vehicle appeared at the rear end of the motorcycle. In the IBS condition, 70% of the trials provided accurate information about the potential hazards. In another 15% of the trials, the buzzer sound warning was given, while there was no vehicle at the rear end of the motorcycle. In the remaining 15% of the trials, there was a vehicle approaching from the rear end; however, no warnings were given to the motorcyclist.

Also, in the IVS condition, 70% of the trials provided accurate information about the potential hazards. In another 10% of the trials, wrong vehicle information about the potential hazard was given; e.g., the approaching vehicle was "Car", but the warning was given as "Alert Motorcycle". In another 10% of the trials, there was no approaching vehicle at all; however, the motorcyclist was still given a warning about the potential hazard. In the remaining 10% of the trials, no warning was given to the motorcyclists even though there was a vehicle approaching from the rear end.

In the PVS condition which produced 100% reliable warnings, the motorcyclist was always given a prior auditory verbal warning about the potential hazard approaching from the rear end.

The motorcyclists were instructed to increase the speed (not to increase more than 90 km/h) or change the lane to avoid the potential accidents from the rear end.

The experiment was conducted in one session that lasted approximately two and half hours. The participants were instructed not to take any coffee or tea four hours prior to the experiment. They were encouraged to keep their muscles and eye movements minimum throughout the task. They could take breaks between blocks to minimize tiredness and eye movements.

Data Acquisition

EEG data were acquired by using Enobio-20, 24-bit ADC per channel sampled at 500 Hz. NIC v1.4 software was used to record the data. Also, during the EEG data 94 recording, a low pass noise filter of 50 Hz has been applied to minimize the noise effect. The markers for ERP analysis were sent by using Matlab 2015a.

A total number of twenty EEG electrodes (FP1, FP2, F7, F8, F3, F4, Fz, T7, P7, against different types of rear collision warning systems are also reported in this chapter.

Vehicle Detection and Classification

In this study, visual/audio-based techniques are used for vehicle detection and an audio-based technique is used for vehicles classification. Finally, these techniques were merged together to design a rear-end vehicle collision detection system and classification system. This was implemented on the Intel(R) Core(TM) i7-4770 CPU (3.4 GHz processor dual-core, installed memory 16GB) and investigated in visual C++. The proposed method is evaluated accordingly to the recent standard presented in Table 4.1 and the following equation.

Vision-based Vehicle Detection

Vision-based vehicle detection technique was investigated in C++ using OpenCV. Our method was evaluated on the self-recorded dataset as well as on the online datasets [START_REF] Sivaraman | A general active-learning framework for on-road vehicle recognition and tracking[END_REF][START_REF] Rezaei | Vehicle detection based on multi-feature clues and Dempster-Shafer fusion theory[END_REF][START_REF] Choi | Realtime on-road vehicle detection with optical flows and Haar-like feature detectors[END_REF] For reduced features set obtained by the SVD, the maximum TPR values of obtained by using the reduced features are lower as compared to the results obtained without feature reduction (i.e., 95.47%, and 5.51%). However, SVD significantly reduced the number of features (i.e., from 1765 to 249). This feature reduction will lead to the projection of features in less dimensional space and enable the classifier to make a fast and reliable and fast decision. Hence, the reduction of this feature set will further enhance the performance of the classifier. Also, the TPR values obtained for all four classes by using feature matrices for F5 and F6 are higher as compared to the values obtained by using any other feature matrix. This is due to the all classes' energy and power features which exist in lower frequency components [START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF][START_REF] Borkar | Review on vehicular speed, density estimation and classification using acoustic signal[END_REF][START_REF] Tatić | Analysis of noise level generated by helicopters with various numbers of blades in the main rotor[END_REF][START_REF] He | Investigation into external noise of a high-speed train at different speeds[END_REF][START_REF] Sharp | Status of Low-Frequency Aircraft Noise Research and Mitigation[END_REF]. For example, for helicopter class, most of the noise is due to its rotating blades and is dominant in the frequency range of 30 to 2000 Hz [START_REF] Tatić | Analysis of noise level generated by helicopters with various numbers of blades in the main rotor[END_REF].

Audio-based Vehicle Classification

Similarly, engine noise (i.e., which consists of moving vehicle sound signatures), train and airplane noises also appeared in lower frequency spectrum [START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF][START_REF] Borkar | Review on vehicular speed, density estimation and classification using acoustic signal[END_REF][START_REF] He | Investigation into external noise of a high-speed train at different speeds[END_REF][START_REF] Sharp | Status of Low-Frequency Aircraft Noise Research and Mitigation[END_REF]. The airplane noise is associated with the engine exhaust and internal system noise. The internal system noise is due to the compressor and turbine blade rotations [START_REF] Sharp | Status of Low-Frequency Aircraft Noise Research and Mitigation[END_REF]. From Table 4.5, the proposed methodology obtained higher true positive rate (i.e., precision) for many classes (i.e., sixteen out of nineteen classes) as compared to the existing state of the art from A. Rakotomamonjy and G. Gasso [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] study. A notable difference has been found in the true positive rate of cafe (4), market (8), metro Paris (9), metro Rouen [START_REF] Rahman | Burden of motorcycle-related injury in Malaysia[END_REF], quiet street [START_REF] Manan | Factors associated with motorcyclists' speed behaviour on Malaysian roads[END_REF], restaurant [START_REF] Demarco | The impact response of motorcycle helmets at different impact severities[END_REF], pedestrian street [START_REF] De Rome | Motorcycle protective clothing: protection from injury or just the weather?[END_REF] and shop ( 16) as compared to the existing state of art method [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF]. Also, the higher results are obtained for many other classes such as plane (1), busy street (2), bus (3), train station hall (6), kid game hall [START_REF] Noland | Has the great recession and its aftermath reduced traffic fatalities?[END_REF], student hall [START_REF] Abbas | Does wearing helmets reduce motorcycle-related death? A global evaluation[END_REF], train [START_REF] Fowler | An Examination of Motorcycle Antilock Brake Systems in Reducing Crash Risk[END_REF] and tube station [START_REF] Filippi | Electronic stability control for powered two-wheelers[END_REF] as compared to the existing method [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF].

For the car, billiard pool hall and high-speed train classes, the obtained results (i.e., 99.10%, 98.69% and 99.43%) are less as compared to the A. Rakotomamonjy and G. Gasso study [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF]. A. Rakotomamonjy and G. Gasso [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] method obtained 100% results for a billiard pool hall and high-speed train classes. While for the class car, true positive rate (i.e., precision) of 99.90% is reported in the literature [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF].

Overall, the true positive rate of 96.58% is achieved for the given methodology, which is significantly higher than the A. Rakotomamonjy and G. Gasso (i.e., 91.70%) study [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF].

The results show that our technique successfully classifies the vehicle for the motorcycle (using the self-recorded dataset) application and as well as for the online audio-based classification datasets. It can successfully classify the incoming vehicles perfectly from the rear end on single/multiple lanes and can be helpful to minimize accidents.

Fusion of Vehicle Detection and Classification

In Therefore, the alert warning is given for that vehicle only.

EEG based Physiological Response Study for Motorcyclists

The analysis of ERP components N1, N2, P3, and N400 to investigate the motorcyclist response against collision warning system are given below in subsections. shows that the mean amplitudes of N1, N2, P3, and N400 decreased for all three auditory warning conditions compared to the no sound system condition. The lowest values of mean amplitudes of these ERP components are noted for the PVS condition (i.e., when the auditory-verbal warnings were 100% reliable). Also, the latencies of these ERP components have been shifted early for all three auditory warning conditions verses NSS condition. Similarly, N2 corresponds to the negativity focused under the electrode of C2. The P3 shows a strong positivity above the central posterior midline (i.e., Pz). The N400 component shows the negativity focused on the frontal region under the electrode of Fz. A decrease in mean latency and amplitude has been observed for different auditory warnings systems compared to the scenario when there was no collision warning system. Also, these auditory systems have positive impact of drivers, i.e., they need less attentional resources to process the imminent threat information for these auditory warning systems.

Latency and Amplitude Analysis for ERP Components

One Way Repeated Measures ANOVA Results

The Shapiro-Wilk test at first used for analyzing the normality of the EEG data as given in Table 4.7 below, as it is more suitable for small sample sizes (< 50 samples). The results of ANOVA with latency (ms) and amplitude (µV) of N1, N2, P3 and N400 components with four warning conditions as factors are given in Table 4.8 below.

From Table 4.8, a statistically significant variation in latency and amplitude of different ERP components has been observed for the NSS condition compared to all three auditory sound conditions. For example, in comparison of NSS condition and IBS condition, a statistically significant reduction in latency (of N1 and P3) and amplitude of N1 has been observed. Also, the latency (of N2 and P3) and amplitude of N2 decrease for the above warning condition comparisons; but these differences were not so much significant.

Furthermore, the latency (of N1, P3, and N400) and amplitude of (N1, N2, and N400) ERP components significantly decreases for IVS condition compared to NSS condition. Moreover, the amplitude and latency of all ERP components statistically significantly decrease for the PVS condition compared to the NSS condition.

123 From Table 4.8, based on the statistical analysis a significant difference has been observed in latency (of P3 and N400) and amplitude of (N2 and N400) for IBS condition in comparison of IVS warning condition. Similarly, in comparison of IBS warning condition and PVS warning condition, a significant drop in amplitude and latency has been obtained for all the ERP components.

If we put side by side IVS warning condition and PVS condition, a statistically significant drop in N1 latency and decrement in amplitude (of N1, N2, and P3) has been noted. Also, a partial less significant decrease in N2 amplitude has been reported for these warning conditions. Overall, no significant effect has been found between both gender groups (i.e., male and female) for four warning condition as factors.

Discussion

The aim of this experiment was to investigate the effectiveness of motorcycle rear-end collision warning system for the auditory warnings conveying information about the incoming vehicle and its kind.

The main findings of this work can be summarized as follows:

• A perfect auditory verbal warning system had a positive effect on neural levels at motorcyclist compared to the no sound system. The analysis of the neural activity suggested a reduction of the time and the attention resources required for processing the target correctly.

• Both imperfect warning systems had some positive effects on motorcyclist performance compared to the no sound system, showing the reduction of the time and the attention resources at the neural level.

• A perfect auditory verbal system also performed better as compared to both imperfect warning systems as it required less time and the attentional resources for processing the target correctly.

The amplitude of N1 decreased for all auditory warning systems compared to the NSS condition. Similarly, the amplitude of N2 decreased (for IVS and PVS warning conditions) and P3 decreased for P3 decreased for PVS warning condition compared to the NSS warning condition. This could be due to the temporal expectations following the auditory alert [START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF][START_REF] Correa | Temporal attention enhances early visual processing: A review and new evidence from eventrelated potentials[END_REF].

The latency of N1 and P3 has been shifted early for all auditory warning conditions compared to the NSS warning condition. Additionally, the latency of N2 has been shifted early in a PVS condition compared to the non-warning conditions. In all auditory warning conditions, the participant expected some target-occurrence. This can shift the latency towards early timeline; as the focusing attention on a point in time improves the behavioral performance and increases the preparatory activity timelocked to the expected target occurrence [START_REF] Seibold | Does temporal preparation increase the rate of sensory information accumulation?[END_REF].

Due to the usage of auditory warnings, the amplitude and latency of N400

have also been investigated. The amplitude N400 was higher for NSS condition compared to the IVS and PVS warning systems. Similarly, the latency also appeared delayed in time for these warning conditions. This could be due to the lack of understanding about the incoming information of the approaching vehicle, which leads to the lack of comprehensive action and triggers a large and long-lasting N400 response [START_REF] Proverbio | RP and N400 ERP components reflect semantic violations in visual processing of human actions[END_REF][START_REF] Kmiecik | Semantic distance modulates the N400 event-related potential in verbal analogical reasoning[END_REF].

The perfect auditory verbal system induced a greater facilitation effect as compared to the non-warning condition at the sensory level and results are consistent The amplitude of the N1 and P3 decreased for PVS condition compared to both imperfect auditory warning systems; while the amplitude of N2 only decreases for PVS warning condition compared to the IVS condition. This might be because the participant needs fewer attentional resources to process the less visual information about the target when the warning system works perfectly [START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF].

Similarly, the latency of P3 shifted early in a PVS condition as compared to both imperfect warning conditions. In addition, the latency of N1 and N2 shifted early in the PVS condition compared to the imperfect buzzer condition. There were fewer chances of the occurrence of a visual target for both imperfect auditory warning conditions as compared to the PVS warning condition; where the participants were 100% sure about the occurrence of the visual target. Therefore, this could be due to the focusing attention on a point in time [START_REF] Seibold | Does temporal preparation increase the rate of sensory information accumulation?[END_REF]. The N2 and P3 latency shift can also be related to cognitive control (i.e., monitoring or regulation of strategy) [START_REF] Folstein | Influence of cognitive control and mismatch on the N2 component of the ERP: a review[END_REF].

The amplitude of N400 decreased and latency shift early in PVS condition, compared to the imperfect buzzer warning condition. The imperfect buzzer warning only alerts about the potential hazard but did not provide any information about the approaching vehicle. This could lead to the delayed action and trigger a large and delayed response of the N400 component [START_REF] Proverbio | RP and N400 ERP components reflect semantic violations in visual processing of human actions[END_REF][START_REF] Kmiecik | Semantic distance modulates the N400 event-related potential in verbal analogical reasoning[END_REF]. The decrement of N400 amplitude and shortness of latency for PVS as compared to the IBS can also be linked with the semantic incongruence [START_REF] Ortega | Voluntary modulations of attention in a semantic auditory-visual matching Task: an ERP study[END_REF].

The neurophysiological results show that the perfect auditory verbal warning system, induced a greater facilitation effect as compared to both imperfect warning systems at the sensory level and the results are consistent with the A. Fort et. al., 2013 [79] study.

The imperfect auditory verbal warning system is also found more effective as compared to the imperfect buzzer warning system. Both systems have an accuracy of 70%. The amplitude of N2 and N400 decreased for IVS condition compared to the IBS condition. Also, the latency of P3 and N400 shifted early in the IVS condition.

The absence of effect on the N1 amplitude suggests that the cognitive load did not alter early sensory processes and had only an impact on the later attentional process.

The verbal auditory warnings give the better perception about the potential danger [START_REF] Meng | Tactile warning signals for in-vehicle systems[END_REF] and are less irritating as compared to the buzzer warnings [START_REF] Politis | To beep or not to beep?: Comparing abstract versus language-based multimodal driver displays[END_REF]. Therefore, it may be possible that verbal information affects the attentional process. Also, the information about vehicle category leads to the decrease in the N400 amplitude and shortens it latency [START_REF] Proverbio | RP and N400 ERP components reflect semantic violations in visual processing of human actions[END_REF][START_REF] Kmiecik | Semantic distance modulates the N400 event-related potential in verbal analogical reasoning[END_REF].

CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

This chapter provides the conclusion of the present work. Besides, this chapter also includes some suggestions for the future work related to the audio-based vehicle classification system. Finally, some recommendations are given to investigate the motorcyclists' physiological responses for mobile phone distraction scenarios for the rear end auditory verbal warning system.

Conclusion

From this work, it can be concluded that the proposed technique is effective for The proposed method provides reliable results, making it more trustworthy for motorcycle and other vehicle applications. It will provide a platform to detect and classify the vehicles for collision warning system based on their acoustic signatures. It will help to reduce the motorcyclist fatality rate when integrated with an auditory verbal warning based alert system.

Finally, the physiological responses of motorcyclists investigated by using ERP components N1, N2, P3, and N400, respectively, for the rear end collision warning systems. The auditory information about the potential danger further increases the physiological responses of the drivers. When the collision warning system was imperfect, still it assists the motorcyclist to avoid the rear end collision. The analysis of the neural activity suggested that in the presence of collision warning system, a reduction in the time and the attention resources has been observed to process the target correctly. Furthermore, verbal information about the potential hazard improves the behavioral performance and increases the preparatory activity time-locked to the expected target-occurrence. Finally, no significant difference has been found in different gender groups.

The installation of collision warning systems in motorcycles can help to reduce a significant number of motorcycle accidents and can save many lives. In summary, the following tasks have been achieved:

• The vehicles approaching from the rear end of the motorcycle have successfully been detected by using the visual appearance features and it is (i.e., younger or older) can adopt this system more quickly. Finally, the effectiveness of this auditory collision warning system can also be investigated in the presence of cognitive secondary tasks. The motorcyclists can be given secondary tasks likewise attending phone call or listening music while driving, and the effectiveness of these auditory verbal warnings can be studied.

  Deux types d'ensembles de données, à savoir des ensembles de données acquises durant ce travail (obtenues en plaçant une caméra à l'arrière d'une motocyclette) et des ensembles de données disponibles téléchargeables (pour la détection visuelle et pour la classification audio des véhicules) sont utilisés pour la validation. La méthodologie proposée a permis de détecter et de classer les véhicules pour des ensembles de données acquises durent cette thèse. De même, pour les ensembles de données disponibles, le taux de vrais positifs le plus élevé et le taux de fausse détection le plus faible ont été atteints par rapport aux méthodes de l'état de l art. En outre, une étude physiologique basée sur le potentiel lié à l'événement (ERP) a été réalisée sur les motocyclistes afin d'étudier leurs réponses vis-à-vis du système d'alerte de collision arrière. Deux types d'avertissements auditifs (c'est-à-dire verbal et buzzer) sont utilisés pour ce système d'avertissement. Pour étudier la réponse des motocyclistes, les composantes N1, N2, P3 et N400 ont été extraits des données d'électroencéphalographie (EEG). Ces systèmes d'avertissement ont montré des effets positifs au niveau neuronal sur les motocyclistes et réduisent leur temps de réaction et les ressources attentionnelles nécessaires pour traiter correctement la cible. En résumé, le système d'avertissement de collision par l'arrière proposé avec des avertissements verbaux auditifs augmente considérablement la vigilance du motocycliste et peut être utile pour éviter les scénarios possibles de collision arrière. Title : Visual and Acoustic based Techniques for Motorcycle Collision Warning System with EEG Validation.

7 :

 7 Features used to analyze the response of drivers. ....................................... Table 2.8: EEG wave band frequencies. ...................................................................... Table 2.9: EEG responses of drivers for different warning systems. .......................... Table 3.1: Self-recorded multimedia dataset characteristic. ........................................ Table 3.2: Datasets for vision-based vehicle detection. ............................................... Table 3.3: Audio datasets characteristic for vehicle classifications. ........................... Table 3.4: Number of features obtained by using SVD for different data balancing techniques. ................................................................................................................... Table 3.5: Frequency band of the audio signal using 5 th level of decomposition. ....... Table 3.6: Number of features obtained for the different dataset. ............................... Table 3.7: Number of features obtained for the different dataset. ............................... Table 3.8: Number of features obtained by SVD. ....................................................... Table 3.9: First frame analysis method for the combination of vehicle detection and classification techniques. ............................................................................................. viii Table 3.10: K-map values for the first frame of any N th vehicle. ................................ Table 3.11: Combination of vehicle detection and classification techniques for any frame (i.e., other than the first frame). ......................................................................... Table 3.12: K-map values for any next frame of the N th vehicle. ................................ Table 3.13: Sample size for different power values. ................................................... Table 3.14: Participants selection details [72-79, 159-161]. .......................................Table 3.15: Verbal warnings given to the motorcyclist. .............................................. Table 4.1: Confusion matrix for the proposed technique to calculate different parameters. ................................................................................................................... Table 4.2: Results of the proposed visual technique. ................................................. Table 4.3: Different class detections by using feature matrix F7 from the self-recorded dataset. ....................................................................................................................... Table

Table 4 . 5 :Figure 1 . 1 : 2 Figure 1 . 2 : 2 Figure 2 . 1 : 13 Figure 2 . 2 : 35 Figure 2 . 3 : 40 Figure 3 . 1 : 51 Figure 3 . 2 : 52 Figure 3 . 3 : 55 Figure 3 . 4 : 56 Figure 3 . 5 : 56 Figure 3 . 6 : 58 Figure 3 . 7 : 59 Figure 3 . 8 : 60 Figure 3 . 9 :Figure 3 . 10 : 62 Figure 3 . 11 : 63 Figure 3 . 65 Figure 3 . 13 : 67 Figure 3 . 71 Figure 3 . 15 : 72 Figure 3 . 16 : 76 Figure 3 . 17 : 77 Figure 3 . 18 : 79 Figure 3 . 19 : 80 Figure 3 . 20 : 85 Figure 3 . 21 :Figure 3 . 22 : 90 Figure 3 . 23 : 91 Figure 3 . 24 : 92 Figure 3 . 25 : 94 Figure 3 . 26 : 95 Figure 3 . 27 : 95 Figure 4 . 1 : 98 Figure 4 . 2 :

 451121222113223523403151325233553456355636583759386039310623116336531367371315723167631777318793198032085321322903239132492325943269532795419842 Figure 1.1: Honda (CD-70) motorcycle used in Pakistan. ............................................. 2

Figure 4 . 3 : 100 Figure 4 . 4 : 103 Figure 4 . 5 : 103 Figure 4 . 6 : 104 Figure 4 . 7 :

 4310044103451034610447 Figure 4.3: False vehicle detection: a) wrong lane vehicle detection in self-recorded dataset; b) False detection from self-recorded dataset; c) LISA-Dense (frame 735); d) LISA-Urban (frame 13); e) LISA-Sunny (frame 104); and f) Source-2 (frame 744). .................................................................................................................................... 100

Figure 4 . 8 :

 48 Figure 4.8: False detection rate for different data balancing techniques. .................. 105

Figure 4 . 9 : 108 xiiFigure 4 . 10 :

 49108410 Figure 4.9: Vehicle classification from self-recorded dataset: a) Two cars; b) Bus; c) Truck and d) Multiple types of vehicles. ................................................................... 108

Figure 4 . 11 :

 411 Figure 4.11: False detection rate for different data balancing techniques. ................

Figure 4 . 12 :

 412 Figure 4.12: Four classes' detection from the online dataset ESC-50 [150]. ............

Figure 4 .

 4 Figure 4.13: classification of LITIS Rouen [151]. .....................................................

Figure 4 . 14 :

 414 Figure 4.14: Vehicle detection classification from self-recorded dataset: a) Two cars; b) Bus; c) Truck and d) Multiple types of vehicles. ..................................................

Figure 4 . 15 :

 415 Figure 4.15: Auditory verbal warnings: a) Single car at the rear of the motorcycle and b) Two cars. ...............................................................................................................

Figure 4 .

 4 Figure 4.16: Average ERP components for subjects a) N1 Latency; b) N2 Latency; c) P3 Latency; d) N400 Latency; e) N1 Amplitude; f) N2 Amplitude; g) P3 Amplitude and h) N400 Amplitude. ............................................................................................

Figure 4 . 17 :

 417 Figure 4.17: Average ERP plots a) N1 (P8); b) N2 (C2); c) P3 (Pz) and d) N400 (Fz). ....................................................................................................................................

Figure 4 . 18 :

 418 Figure 4.18: Topography of the ERP components analyzed for four warning conditions a) N1 (160ms); b) N2 (200ms); c) P3 (310ms); d) N400 (450ms) and e) Scale for all topographical plots. ...............................................................................

  Figure 1.1 below shows the Honda CD-70 motorcycle used in Pakistan. While Figure 1.2 shows a Yamaha motorcycle (115cc) used in Malaysia.

Figure 1 . 1 :

 11 Figure 1.1: Honda (CD-70) motorcycle used in Pakistan.

Figure 1 . 2 :

 12 Figure 1.2: Yamaha (115cc) motorcycle used in Malaysia.

  performed. Different research areas (i.e., vision-based vehicle detection for the motorcyclist, audio-based vehicle detection and classification, a fusion of audio and visual techniques, and the physiological response study of motorcyclists) are investigated in this research study. Therefore, instead of the in-depth study of any individual research field, the work is more focused on the interaction and fusion of these different research areas.

  techniques have been proposed to increase the safety of motorcyclists and to minimize road fatalities. These techniques can be divided into passive safety systems and active safety systems, as shown in Figure2.1 below.

Figure 2 . 1 :

 21 Figure 2.1: Classification of motorcyclist safety systems.

  audiovisual based techniques classify the vehicles into seven categories. The visual dataset is recorded by using Sony hand-cam with a resolution of 640 by 480. The internal microphone of the hand-cam is used for audio recording. Initially, an audio-based STE feature is used for vehicle detection in both techniques.Afterwards, in[START_REF] Piyush | Vehicle detection and classification using audio-visual cues[END_REF] Bessel's low pass filter is used to remove high-frequency fluctuations. Once the vehicle detection is done by using audio features, then the visual technique is used to classify the detected vehicles. The accuracy of the vehicle detection and classification is not provided in this technique. Also, the audio-based vehicle detection by using a single microphone cannot provide the exact location of the approaching vehicle. While, C. Daniel and L. Mary[START_REF] Daniel | Fusion of audio visual cues for vehicle classification[END_REF] performed vehicles classification by using both audio (i.e. MFCC features) and visual features (i.e. area, perimeter, length of the object and the shape of the object) respectively. Finally, Artificial Neural Network (with 49 input layers, 3 hidden layers, and 7 output layers) is used for classification.

Figure 2 . 2 :

 22 Figure 2.2: Polar pattern of a directional microphone.

  Electroencephalography (EEG) was invented by the Russian physiologistVladimir Vladimirovich Pravdich-Neminsky in 1912[START_REF] Haas | Richard Caton (1842-1926), and electroencephalography[END_REF]. He published the first animal EEG (Dog) signals. Later, in 1924 a German physiologist and psychiatrist, Hans Berger, recorded the EEG signals for the first time from a human being and introduced alpha and beta waves. Hangs Berger also introduced the term "electroencephalogram". Soon after in the mid-1930s, Alfred Loomis showed that in humans EEG patterns dramatically changed during a night's sleep[START_REF] Haas | Richard Caton (1842-1926), and electroencephalography[END_REF].

Figure 2 .

 2 3 shows the position of EEG electrodes in the 10-20 International System.

Figure 2 . 3 :

 23 Figure 2.3: 10-20 international system for positioning of EEG electrodes.

  expensive as compare to the camera. The camera-based techniques give very reliable results as the images are very rich in information. In vision-based techniques, appearance-based edges detection techniques are found more useful to detect the vehicles as compared to other visual techniques. Also, vision-based vehicle classification techniques have been found in the literature. But, the vehicle classification from images is still a challenging task. This is due to their high intraclass variations; many types of vehicles belonging to the same class have features of various sizes and shapes. Moreover, occlusion, shadow, and illumination make the classification task even more challenging. Audio microphones can also to detect and classify the vehicles. Many techniques have been proposed in the literature for the audio-based vehicle detection and classification for surveillance application. Also, audio-visual based vehicle detection and classification techniques have been proposed for surveillance

Figure 3 . 1 :

 31 Figure 3.1: Proposed methodology.

Figure 3 . 2 :

 32 Figure 3.2: Equipment mounted at the rear end of motorcycle.

Figure 3 . 4 :

 34 Figure 3.4: Location of the selected patches in frame

Figure 3 . 5 :

 35 Figure 3.5: Different lane marks present on the road using self-recorded dataset.

Figure 3 . 6 :Figure 3 .7 below. 59 Figure 3 . 7 :

 3635937 Figure 3.6: Binary road detection: a) Self recorded dataset with two cars at lane markers; b) Self recorded dataset with bus footprints; c) LISA-Dense (frame 735); d) LISA-Urban (frame 13); e) LISA-Sunny (frame 104); f) Source-2 (frame 140); g) iROADS-Daylight; and h) iROADS-Tunnel.

Figure 3 . 8 :

 38 Figure 3.8: Sobel edge detection with Otsu threshold; a) Self-recorded dataset with two cars at lane markers; b) Bus footprints from self-recorded dataset; c) LISA-Dense (frame 735); d) LISA-Urban (frame 13); e) LISA-Sunny (frame 104); f) Source-2 (frame 140); g) iROADS-Daylight; and h) iROADS-Tunnel.

Figure 3 . 9 :

 39 Figure 3.9: Lane Marker Free (LMF) image: a) Self-recorded dataset with two cars at lane markers; b) Self-recorded dataset with bus footprints; c) LISA-Dense (frame 735); d) LISA-Urban (frame 13); e) LISA-Sunny (frame 104); f) Source-2 (frame 140); g) iROADS-Daylight; and h) iROADS-Tunnel.

Figure 3 . 10 :Figure 3 . 11 :

 310311 Figure 3.10: Harris corner detection: a) Self-recorded dataset with two cars at lane markers; b) Self-recorded dataset with bus footprints; c) LISA-Dense (frame 735); d) LISA-Urban (frame 13); e) LISA-Sunny (frame 104); f) Source-2 (frame 140); g) iROADS-Daylight; and h) iROADS-Tunnel.

9 )

 9 If (xr, yr) is the position of the corner detected by the Harris corner detection technique, then its slope m and distance d with respect to the initial point of the selected line is given as: mh, m and d permit us to assess if the selected line is passing through the Harris corner landmark, or if it is very close to it (up to four pixels); in that case, it is selected as vehicle footprints. If the selected line intersects the Harris corner then the values of both slops (i.e. mh and m) will be equal. Similarly, the value of d provided the information that how far is the Harris corner from the initial point of the selected line.

Figure 3 .

 3 Figure 3.12: A proposed methodology for audio-based vehicle detection.

  ,ω) is the output signal of STFT and w[n] is the sliding window that emphasizes the local frequency parameter. There are many types of sliding windows which can be used for STFT. The Hann window is selected as the sliding window.The equation for Hann window is given as:

  vehicle footprints, a power spectrogram has been calculated. The power spectrum computed by squaring the magnitude of the STFT signal y(n,ω) ,ω) is the spectrogram of the audio frames. The numbers of features in these frames are considered as power spectrum values. The total number of features in S(n,ω) were 1765 (i.e., S € R 1765 ).

Figure 3 .

 3 Figure 3.13 below shows the spectrogram of the seventy-two seconds audio data. The x-axis represents the duration at which the features appeared; therefore, it can easily be synchronized with video frames for the validation of the technique.

Figure 3 . 13 :

 313 Figure 3.13: Spectrogram of 1:12 (min:sec) duration from the self-recorded dataset.

Figure 3 .

 3 Figure 3.14: A proposed methodology for audio-based vehicle classification.

Figure 3 .Figure 3 . 15 :

 3315 Figure 3.15 below shows the frequency responses of the original and filtered audio signals.

Figure 3 . 16 :Figure 3 . 17 :

 316317 Figure 3.16: Percentage of energy for self-recorded dataset containing multiple vehicles a) E1; b) E2; c) E3; d) E4; e) E5 and f) E6.

6 .

 6 The numbers of features for LITIS Rouen[START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] dataset for features are given in equation 3The power spectrums of the self-recorded dataset and for the online dataset are given inFigures 3.18 and 3.19 respectively.

Figure 3 . 18 :Figure 3 . 19 :

 318319 Figure 3.18: Spectrogram for self-recorded dataset containing multiple vehicles a) S1; b) S2; c) S3; d) S4; e) S5 and f) S6.

A

  vision-based and audio-based vehicle detection techniques provide us the information about the presence of the vehicle at the rear end of the motorcycle. An audio-based vehicle classification technique provides the type of the potential hazard.

Figure 3 . 20 :

 320 Figure 3.20: Window selection to alert motorcyclist.

Figure 3 . 21 :

 321 Figure 3.21: Directional audio pattern of the At-2035 microphone.

Figure 3 . 22 :

 322 Figure 3.22: Motorcycle simulator used for EEG data recording.

Figure 3 . 23 :

 323 Figure 3.23: EEG data recording using Enobio-20.

Figure 3 . 24 :

 324 Figure 3.24: Experimental design for four warning conditions.

  T8, P8, C1, C2, Cz, P3, P4, PZ, O1, O2, Oz) were used to record the participants' EEG signals. The EEG electrode Oz was connected to the EXT electrode for the EEG data recording. The reference electrode was placed near the right earlobe. The EEG electrodes were placed according to the 10-20 international system as shown in Figure3.25 below.

Figure 3 . 25 :

 325 Figure 3.25: Placement of electrodes in Enobio-20 for EEG data recording.

Figure 3 .Figure 3 .

 33 Figure 3.26: ERP epochs extraction from the EEG data.

Figure 3 . 27 :

 327 Figure 3.27: Artifacts detection in ERP epochs.

Figure 4 . 1 :

 41 Figure 4.1: Vehicle detection from self-recorded dataset: a) Two cars crossing the motorcyclist; b) Bus approaching from the rear end; c) truck at the rear end of the motorcycle; and d) Two different types of vehicles.

Figure 4 . 2 :Figure 4 . 3 :

 4243 Figure 4.2: Vehicle detection from online datasets: a) LISA-Dense (frame 735); b) LISA-Urban (frame 13); c) LISA-Sunny (frame 104); d) Source-2 (frame 140); e) iROADS-Daylight; and f) iROADS-Tunnel.

  and 4.5 below show the vehicle approaching towards the motorcyclist at the rear and its audio power spectrum.

Figure 4 . 4 :

 44 Figure 4.4: Small 1000c vehicle at the rear end of the motorcycle.

Figure 4 . 5 :

 45 Figure 4.5: Vehicle of 1300cc approaching the motorcyclist.

Figure 4 .

 4 Figure 4.6 represents the frame when there is no vehicle at the rear end of the motorcycle. From this figure, we can see that when there is no vehicle at the rear end of the motorcycle, the power spectrum can easily be differentiated from the vehicle power spectrum (Figure 4.4 and Figure 4.5).

Figure 4 . 6 :

 46 Figure 4.6: No vehicle at the rear end of the motorcycle.

Figure 4 . 7 :

 47 Figure 4.7: True positive rate for different data balancing techniques.

  For audio-based vehicle classification, a random forest classifier (with 50 numbers of trees) is used to classify the acoustic features. To evaluate the performance, a 5-fold cross-validation method (i.e., 80% training and 20% testing) is used. The proposed method successfully classified the vehicle for self-recorded dataset into the car, motorcycle, bus, truck, multiple type vehicles and no vehicle categories, respectively. It can successfully classify the incoming vehicles perfectly from the rear end on single and on multiple lanes under various conditions and different road scenarios.

Figure 4 .

 4 Figure 4.9 below shows some of the vehicles that are classified from the selfrecorded dataset. From Figure 4.9, different types of vehicles approaching towards the motorcycle from the rear end are successfully classified. If one or more vehicles of the same type are found, then these are classified into that vehicle class (e.g., Figure 4.9 (a)). Similarly, if two or more vehicles belonging to different categories appeared simultaneously at the rear of the motorcycle, then these vehicles are classified as multiple types of vehicle (Figure 4.9 (d)).

Figure 4 . 9 :Figure 4 .

 494 Figure 4.9: Vehicle classification from self-recorded dataset: a) Two cars; b) Bus; c) Truck and d) Multiple types of vehicles.

Figure 4 . 10 :

 410 Figure 4.10: True positive rate obtained by using different feature matrices for the self-recorded dataset.

Figure 4 . 12 :

 412 Figure 4.12: Four classes' detection from the online dataset ESC-50 [150].

Figure 4 .

 4 Figure 4.13: classification of LITIS Rouen [151].

Class 1 :

 1 Plane, Class 2: Busy street, Class 3: Bus, Class 4: Cafe, Class 5: Car, Class 6: Train station hall, Class 7: Kid game hall, Class 8: Market, Class 9: Metro paris, Class 10: Metro rouen, Class 11: Billiard pool hall, Class 12: Quiet street, Class 13: Student hall, Class 14: Restaurant, Class 15: Pedestrian street, Class 16: Shop, Class 17: Train, Class 18: High speed train, Class 19: Tube station.

  the fusion technique, the vision-based vehicle technique is used along with the audio-based vehicle detection and classification method. For audio-based vehicle detection, reduced balanced feature matrix (obtained by using SMOTE-B2 data balancing technique) is used with the RF-50 classifier (i.e., the RF classifier with 50 numbers of trees). While for audio-based vehicle classification feature matrix, F7 is used along with the RF-50 classifier. the alert word and vehicle type. In Figure 4.15 (b), two vehicles are passing by the motorcyclist. Both vehicles are successfully detected and classified. One vehicle almost crossed the motorcyclist. Therefore, a motorcyclist has no chances of the rearend collision with that vehicle. The other vehicle is getting closer towards the motorcyclist and it appeared in the selected danger zone behind the motorcycle.

Figure 4 . 15 :

 415 Figure 4.15: Auditory verbal warnings: a) Single car at the rear of the motorcycle and b) Two cars.

Figure 4 .

 4 Figure 4.16 below shows the box diagram of N1, N2, P3 and N400 (i.e., their latency and amplitude) for four warning conditions. It highlights the distribution of latency and amplitude of four warning conditions for the subject group.Figure 4.16

Figure 4 . 16

 416

Figure 4 . 16 :

 416 Figure 4.16: Average ERP components for subjects a) N1 Latency; b) N2 Latency; c) P3 Latency; d) N400 Latency; e) N1 Amplitude; f) N2 Amplitude; g) P3 Amplitude and h) N400 Amplitude.

Figure 4 .

 4 Figure 4.17 shows the average N1, N2, P3 and N400 components extracted from the EEG electrodes P8, C2, Pz, and Fz, respectively, for all subjects.

Figure 4 . 17 : 120 Figure 4 .

 4171204 Figure 4.17: Average ERP plots a) N1 (P8); b) N2 (C2); c) P3 (Pz) and d) N400 (Fz).

Figure 4 .

 4 18 below gives the topography of the ERP components for four warnings.

Figure 4 . 18 :

 418 Figure 4.18: Topography of the ERP components analyzed for four warning conditions a) N1 (160ms); b) N2 (200ms); c) P3 (310ms); d) N400 (450ms) and e) Scale for all topographical plots.

Further, analysis show

  that there was a statistically significant difference between four warning conditions for the latency of N1: F (3, 25) = 23.37, P = 0.0000002; N2: F (3, 25) = 4.31, P = 0.01; P3: F (3, 25) = 14.83, P = 0.00001 and N400: F (3, 25) = 9.11, P= 0.0003. Also, significant difference has been found in the mean amplitude of the N1: F (3, 25) = 6.72, P = 0.002; N2: F (3, 25) = 11.66, P = 0.00006; P3: F (3, 25) = 9.11, P= 0.0003 and N400: F (3, 25) = 6.49, P = 0.002 for all warning conditions.

  with the A.Fort et. al., 2013 [79] and M.Bueno et al. 2012 [77] studies. Both imperfect warning systems also improved the motorcyclist performance compared to the non-warning systems and it seems to facilitate the sensory processing of the target and results are in line with C.D. Wickens & S.R. Dixon, 2007[START_REF] Wickens | The benefits of imperfect diagnostic automation: A synthesis of the literature[END_REF] and A.Fort et. al., 2013 [79] studies.

  rear-end vehicle detections and classifications for motorcycle applications. Overall, the true positive rate of 95.87% and the false detection rate of 5.25% are obtained for vehicle detections and classifications. Moreover, the average frame rate of 23.25 fps is achieved for the proposed methodology. The vision-based vehicle detection method presented in this paper achieves higher accuracy and better results in different road scenarios compared with other methods recently published. The patch selection method for the binary road detection contributes a lot to reduce the false detections and produce reliable results, even when shadows or different lane markers are present on the road. The size selection of the lines, computed from Hough transform, also helps to avoid the shadow regions and improve the accuracy. It shows a very good performance for the motorcycle using the rear-end dataset as well as utilizing the online vehicle frontal datasets. It achieved higher accuracy, TPR, and frame rate for many road scenarios as compared with the existing state-of-the-art methods. This technique can correctly classify the vehicles into different categories. As the light conditions did not affect acoustic information, the audio-based vehicle detection and classification methods can equally be used for the day and night applications. Due to the usage of the directional microphone; the vehicles moving in opposite directions are automatically ignored. Wavelet decomposition helps us to analyze the vehicle audio signatures in different frequency ranges. The usage of SVD for audio-based vehicle detection and classification methods further increases its processing capability by restraining the number of features. The vehicle classification method achieved exceptional results for online datasets and performed better as compared to the existing state of the art methods. It successfully classifies the vehicles for different scenarios.

TABLE OF CONTENTS

 OF RÉSUMÉ ............................................................................................................... ii

Table 2 .

 2 2: Advantages and disadvantages of collision detection techniques. .............

Table 2 .

 2 3: Related studies for vision-based vehicle detection (continuous). ...............

Table 2 .

 2 4: Related studies for vision-based vehicle classification. .............................

Table 2 .

 2 5: Related studies for audio-based vehicle detection and classification. ........

Table 2 .

 2 6: Related studies for audio-visual based vehicle applications.......................

Table 2 .

 2

Table 1

 1

.1 gives the percentage distribution of motorcycles in different regions of the world.

Table 1 . 1 :

 11 Motorcycles distribution in different regions of the world[START_REF] Muslim | A review on retrofit fuel injection technology for small carburetted motorcycle engines towards lower fuel consumption and cleaner exhaust emission[END_REF].

	Region	Motorcycles (%)
	Asia	78
	Europe	14
	Latin America	5
	North America	2
	Africa	1

Table 2 .1: Related studies for motorcycle collision detection systems. Author/ Year Study type/Data type Collision Detection System Limitations/Conclusions

 2

	E. D.	Different techniques	Multiple	The development and
	Bekiaris et	were proposed for a	sensors.	accuracy of the systems were
	al. (2009)	motorcyclist to avoid a		not presented.
	[20]	collision.
	F. Biral et al.	Tested in a laboratory	Global	A road-curve warning system
	(2010) [21]	environment.	positioning	was presented to increase the
			system (GPS)	safety of motorcyclists.
			device and
			digital map.
	C.-Y. Fang	Sixteen sequences	Mobile	For the given technique, a
	et al. (2014)	(containing 60,261	camera and	mobile camera was used to
	[22]	frames) were captured	GPS device.	detect the frontal vehicles and
		by a smartphone and		a GPS device was used to
		used to test the		estimate the distance to the
		performance of the		frontal vehicles. The given
		system.		technique had 98.49%
				accuracy.
	G. Savino et	Experimental tests	Laser Scanner. The study focused on the
	al. (2012)	were conducted in a		activation of an active braking
	[30]	transportation research		system and achieved 98%
		laboratory.		proper detection.
	F.	An experimental-based	Laser Scanner. This research used a laser
	Giovannini	study involving 12		scanner for a motorcycle
	et al. (2013)	volunteers riding a		autonomous emergency
	[31]	scooter equipped with a		braking system. The system
		prototype.		was effective in minimizing
				the chance of collision.
	G. Savino et	A total of 10 tests were	Laser Scanner. No false detection was
	al. (2009)	conducted for both		generated.
	[105]	moving and fixed
		obstacles, while the
		motorcyclist traveled
		towards the obstacles.
	G.	The study was	Camera, range	A camera and range-based
	Kumarasamy	conducted in a test	sensor, and a	sensor were used to detect the
	et al. (2015)	environment by pre-	gyroscope	frontal vehicles. To track
	[106]	calculating the distance	sensor.	motion, a gyroscope sensor
		from obstacles.		was placed on the
				motorcyclist helmet.
	A. Amodio	Controlled overtaking	Radar.	The accuracy of this
	et al. (2017)	scenarios have been in		technique is not presented.
	[107]	this study.
	F. Biral et al. [21] presented a system that warns motorcyclists of an
	approaching curve in the road to avoid accidents. The presented technique (which was
	only tested in a laboratory environment) was based on a Global Positioning System

Table 2 . 3 :

 23 Related studies for vision-based vehicle detection (continuous).

	Author/	Detection	Dataset	True positive	Accuracy	Features/Feature	Limitations
	Year	type		rate (%)	(%)	extraction method/
						Classifier
	M. I.	Front.	3,400 images (for	96.71, 98.41,	97.60,	Shadow underneath the
	Arenado et al. (2014)		vehicle detection).	97.28, 96.80	98.80, 97.90,	vehicle and horizontal edges.
	[39]				96.75
		Rear.	5,725 images.	92.50, 94.80,	-------	Haar wavelet/ Support	The optimization for this technique was
				93.70, 89.10, (RA1 results)		vector machine (SVM).	not performed to satisfy the real-time requirement.
	R. O'malley	Rear.	44 video clips,	92.86
	et al. (2011)		each contains at
	[112]		least one vehicle.
	C. Wu et al.	Left sided. 9 video	87.00, 80.00,	-------	Histogram of gradient
	(2013) [113]		sequences used to	84.00, 95.00,		(HOG) for vehicle
			capture the	84.00, 81.00,		detection/ Neuro-fuzzy
			images.	88.00, 89.00,		network for distance
				62.00		measurement.

To detect the front vehicle license plate, both vehicles should be very close to each other. This can be dangerous under the high-speed scenario. This technique is very slow and can process only four frames in one second. W.

[START_REF] Liu | Rear vehicle detection and tracking for lane change assist[END_REF]

[START_REF] Liu | Rear vehicle detection and tracking for lane change assist[END_REF]

-------Headlamp pairs detection. The technique is only effective at night or when the light condition was very low.

Table 2 . 3 :

 23 Related studies for vision-based vehicle detection (continued).

	Author/	Detection	Dataset	True positive	Accuracy	Features/Feature	Limitations
	Year	type		rate (%)	(%)	extraction method/
						Classifier
	T.-Y. Lee et
	al. (2015)
	[114]

Table 2 . 4 :

 24 Related studies for vision-based vehicle classification.

	Author/ Year	Vehicle Classes	Features/Features extraction method	Classifier	Accuracy (%)
	K. F.	Motorcycle,	Scale-invariant	Random neural	95.63
	Hussain and	Small, Medium	feature transform	networks (RNNs).
	G. S.	and Large.	(SIFT).
	Moussa
	(2016) [45]
	S. Shantaiya	Car and	Area, Perimeter,	Artificial neural	75.00
	et al. (2016)	Motorcycle.	Major Axis, Minor	network (ANN),	82.00
	[46]		Axis, Convex Area	Decision tree, SVM.	90.00
			and Eccentricity.
	G. S.	Multi class and	Geometric based	SVM.	----
	Moussa	Intra class.	and Appearance-
	(2014) [47]		based.
	Z. Xiang et	SUV, Truck,	Vehicle surface	SVM.	94.20
	al. (2016)	Van, Bus, and	texture.
	[48]	Car.
	D. Zhao et	Sedan, Van,	Headlight, Wipers	Convolutional neural	97.93
	al. (2016)	Truck, SUV	and Ventilation	network (CNN)
	[117]	and Coach.	grill.	model based on
				visual attention.
	L. Jiang et	Large bus, Car,	HOG and HU	Sparse representation	96.53
	al. (2016)	Motorcycle,	moments.	based classifier
	[118]	Minibus, Truck		(SRC).
		and Van.
	Z. Dong et	Bus, Microbus,	Convolution,	Softmax Regression. 95.70
	al. (2014)	Minivan,	Contrast
	[119]	Sedan, SUV,	normalization, and
		and Truck.	Average pooling.
	L. Zhuo et	Bus, Car,	-----	GoogLeNet.	98.26%
	al. (2017)	Minibus, and
	[120]	Truck.
	H. Fu et al.	Bus, Car,	Gaussian mixture	Hierarchical
	(2016) [121]	Minibus, Truck,	model.	multiclass SVM.
		and Others.

  geometric based and appearance based approach are used for . F. Hussain and G. S. Moussa[START_REF] Hussain | On-road vehicle classification based on random neural network and bag-of-visual words[END_REF] used histogram (constructed by the Bag of Visual Words (BOVWs)) to classify vehicles into four classes. This method classified the vehicle based on their size. In this method, key points are detected from the image by using Harris-Laplace salient point detector. Subsequently, descriptor features are computed from these key points by using Scale Invariant Feature Transform (SIFT).

	two vehicle classification task named as 1) multiclass vehicle classification and 2)
	intraclass vehicle classification. In multiclass vehicles are divided into small, medium
	and large vehicle categories. While, in intraclass vehicles are classified into the Each descriptor is assigned a visual word and vocabulary is developed. This
	pickup, sports utility vehicle, and van respectively. In this technique for multiclass, vocabulary is used to construct a histogram. Finally, Random Neural Network (RNN)
	both geometric and appearance-based techniques give almost similar results. While is used to classify the vehicles.
	appearance based technique performs better for intraclass vehicle classification. Combination of high level and low-level features can also be used for vehicle
	Generally, geometric-based, methods may not successfully classify the vehicle classification. For example, in [119], low level (i.e., characteristics of vehicle parts)
	types since different types of vehicles may have a similar size [45].
	image.
	L. Jiang et al. [118] used HOG features and HU moments to classify the
	vehicles into six categories for surveillance application. In this research, first images
	are normalized into 64x64 and for HOG features the block size is set to 16x16. A total

Z. Xiang et al.

[START_REF] Xiang | An Effective and Robust Multi-view Vehicle Classification Method Based on Local and Structural Features[END_REF]

used vehicle surface texture to classify the vehicles. They recorded the five surveillance videos for vehicle classifications and also vehicles are classified into five categories; named as 1) SUV, 2) truck, 3) van, 4) bus and 5) car respectively. In this technique, initially RGB images are converted into grayscale and resize it to 100x100. A predefined region of interest (ROI) is used for each image.

Vehicle surface texture is used to classify the vehicles by using Support Vector Machine (SVM). However, this technique is only tested on self-recorded images (obtained from five videos) and did not validate by any online dataset. D. Zhao et al.

[START_REF] Zhao | Deep reinforcement learning with visual attention for vehicle classification[END_REF]

used key areas of the vehicle such as a headlight, a wiper and a ventilation grille for vehicle classification. The vehicles are classified into five major categories named as sedan, van, truck, SUV, and coach respectively. While the sedan is further classified into further fifty-eight maker categories. In this technique, digital fovea technique is applied on the images. The digital fovea is working is like human 'fovea centralis', which helps human to more focus on the one part of the number of 1764 dimensional feature vector is obtained for each image. For HU features extraction each image is divided into 4 blocks. However, the only selfrecorded dataset is used to validate this technique. Kand high-level features (that provide the vehicle holistic description) are used for vehicle classification. The Convolutional Neural Network (CNN) is used to select features for the vehicle classification. In this technique the vehicles are classified into six categories named as 1) bus, 2) microbus, 3) minivan, 4) sedan, 5) SUV, and 6) truck respectively. L. Zhuo et al. [120] used GoogLeNet to classify the vehicles into four categories. GoogLeNet is basically a Convolutional Neural Networks (CNN) network architecture introduced by Google and is widely used for image classification, person identification, and many other fields. In this technique, GoogLeNet with 22 layers has been used for the vehicle classification. This technique only focuses on the GoogLeNet classifier. It is tested on the surveillance datasets. H. Fu et al. [121] proposed a vehicle tracking and classification technique for surveillance application. In this research, a Gaussian Mixture Model (GMM) is used for the foreground detection. Two levels of Support Vector Machine (SVM) (i.e. first order SVM and second-order hierarchical multiclass SVM) are used for the vehicle classification. Finally, voting based correction scheme is used for the vehicle tracking.

Table 2 . 5 :

 25 road data' is recorded by placing the microphone on the roadside. The proposed techniques give a high accuracy of 74.4% for deep learning classifier. Related studies for audio-based vehicle detection and classification. . A. Rahim et al.[START_REF] Rahim | Heterogeneous multi-classifier for moving vehicle noise classification[END_REF] classified the 140 vehicles into car, bike, truck, and lorry respectively. In this research, frequency-based features have been extracted by using one-third-octave frequency spectrum. K-nearest neighbor (KNN), support vector machine (SVM) and multilayer perceptron (PLM) classifiers are used for vehicle classification. The proposed technique has only been tested on the self-recorded dataset.E. Kubera et al.[START_REF] Kubera | Audio-based hierarchic vehicle classification for intelligent transportation systems[END_REF] detected and classified vehicles into a small truck, big truck, van, small car, and tractor respectively. In this study, the audio data is recorded at 48kHz and at 24bit resolution. Fast Fourier Transform (FFT) along with hamming window is used to extract the frequency spectrum. According to the authors, with the increase of frame size, the better frequency resolution can be achieved for the spectrum. This can be helpful to enhance the vehicle classification results.

	Author/ Year	Features / Feature extraction methods	Classifier	Accuracy (%)
	A.	Spectrum Envelope,	SVM, Random Forest	-----
	Wieczorkowska	Spectrum Centroid (SC),	(RF) and Deep
	et al. (2017) [52]	Spectrum Spread (SS) and	learning.
		ZCR
	N. A. Rahim et al.	Frequency spectrum.	SVM, KNN and	-----
	(2013) [54]		Multilayer Perceptron.
	E. Kubera et al.	SC, SS, Energy and ZCR.	SVM, ANN, RF and	85.00
	(2015) [55]		Decision trees.
	E. Alexandre et	MFCC, SC, STE, ZCR,	SVM, ELM, Zero-R	93.74
	al. (2015) [56]	Voice2White (V2W),	classifier (ZeroR),
		Spectral Flux (SF),	Bayesian network
		Percentage of Low Energy	(BayesNet), Gaussian
		Frames (LEF) and Spectral	Radial Basis Function
		Flatness Measure (SFM).	(RBF), 1-Nearest
			Neighbor (1-NN) and
			Best First Decision
			Tree (BFTree).
	A. Mayvan et al.	STE, Short Time Average	SVM, KNN, Linear	80.00
	(2015) [57]	Zero Cross Rate, and Pitch	discriminant analysis
		Frequency.	(LDA) and Quadratic
			discriminant analysis
			(QDA).
	M. P. Paulraj et	AR Model.	Probabilistic Neural	94.50
	al. (2013) [58]		Network (PNN).
	M. L. Shah and P.	ZCR, Root mean square	Adaptive neuro-fuzzy	-----
	D. Mehta (2014)	(RMS), Short time energy	inference.
	[51]	(STE), Centroid mean
		(CMEAN), Centroid standard
		deviation (CSD), Power
		spectral density (PSD) and
		MFCC.
	B. J. Barai and G.	ZCR, STE, RMS and SC.	SVM and ANN.	100
	Kamdi (2014)
	[123]
	J. George et al.	MFCC.	ANN and KNN.	71-74
	(2013) [124]
	M. Górski and J.	MFCC, Harmonic line, and	SVM.	84.00
	Zarzycki (2012)	Schur coefficients.
	[125]
	N. Bhave and P.	MFCC and Formant features. KNN.	-----
	Rao (2011) [126]

5) motorcycle (excluding scooters), 6) car and 7) tractor respectively. Two types of self-recorded datasets (i.e. test bench data and on-road data) are used to validate the proposed techniques. The 'test bench data' is recorded in the libratory environment, while 'on NE. Alexandre et al. [56] classified a total number of 574 vehicles into only three types of vehicles (i.e. cars, motorbikes, and trucks). They recorded the audio data at the sample rate of 11.025 kHz. Genetic Algorithm (GA) has been used for features selection. Extreme Learning Machine (ELM) gives a maximum accuracy of 93.74% for 22 selected features included Mel-frequency Cepstral Coefficients (MFCC) and Zero Crossing Rate (ZCR).

Table 2 . 6 :

 26 and unsafe categories to alert the bicycle rider. The proposed method achieved 69.2% and 80.9% accuracy for visual and audio-based vehicle detection respectively. Overall, it achieved very low accuracy for vehicle detection. Also, it does not classify the vehicle into different categories, which can be more useful to avoid the accidents. Related studies for audio-visual based vehicle applications.Vehicles are also classified as light, medium, and heavy vehicles. The proposed technique achieved maximum accuracy of 94.1% and 93.2% for type based (using ARS, SP, HOG, perceptual features) and size based (using HOG and perceptual features) vehicle classification respectively.

	Author/ Year	Visual Technique	Audio Technique	Applications	Accuracy (%)
	S. Smaldone	Optical flow analysis	FFT and	Bicycle.	78.30
	et al. (2010)	and Principle	Decision tree.
	[127]	component analysis
		(PCA).
	T. Wang and	Aspect ratio and size,	Time series,	Road Side.	94.10
	Z. Zhu	Shape profile, HOG,	Spectral features,
	(2012) [128]	and SVM.	Perceptual
			features and
			SVM
	P. Piyush et	Morphological	STE and	Road Side.	-----
	al. (2016)	operation and	MLFFNN.
	[129]	Multilayer feed-forward
		neural network
		(MLFFNN).
	C. Daniel	The shape of the object	STE, MFFC, and	Road Side.	92.10
	and L. Mary	and ANN.	ANN.
	(2016) [130]
	A. Klausne	Haar-like, center-	Cepstral features,	Road Side.	-----
	et al. (2007)	surround features, and	Spectral features,
	[131]	SVM.	STE, ZCR, and
			SVM.
	Most of these techniques have been proposed for vehicle surveillance
	applications. Such as, T. Wang and Z. Zhu [128] proposed audiovisual based vehicle
	detection and classification technique for surveillance purpose. Visual features named
	as 1) aspect ratio and size (ARS), 2) shape profile (SP) and 3) HOG are used to detect
	the vehicle size and type. While, audio features include short time energy (STE),
	spectral energy, entropy, flux, spectral centroid and MFCC, which are divided into
	three groups. Support vector machine with RBF kernels is used to classify the

the audio-visual technique is used to detect rear end vehicles for bicycle application. This study is the most relevant to our research. For visual technique, the optical flow method is used to track the rear end vehicle and for audiobased vehicle detection Fast Fourier transform (FFT) is used to extract features. A safety zone is created at the rear end of the bicycle. Afterwards, the vehicles are classified into unsafe vehicles. A total number of 485 vehicle samples (280 for training and 205 for testing) are used for the validation. The dataset is recorded on the roadside. The vehicles are classified into four different types' sedan, vans, pickup trucks, and buses respectively.

Table 2 . 7 :

 27 Features used to analyze the response of drivers.

	Author/ Year	Warnings Type	Features	Number of Subjects
	J. Kazazi et al.	Visual.	Brake reaction time, Number of	72
	(2015) [67]		collisions and Maximal braking value.
	X. Yan et al.	Auditory.	Brake reaction time, Collision rate	50
	(2014) [60]		and collision severity and Lane
			deviation.
	J. Fagerlönn.	Auditory.	Brake reaction time, Time to collision	18
	(2010) [59]		and Brakeforce.
	C. Ho et al.	Visual and	Response time, Shortest headway,	11
	(2006) [61]	vibratory.	Brake force and Percentage of
			collisions.
	J. Scott and R.	Visual,	Response time and Time to the	16
	Gray. (2008)	auditory and	collision.
	[62]	tactile.
	R. Mohebbi et	Auditory and	Brake reaction time.	16
	al. (2009) [63]	tactile.
	F. Naujoks et	Visual-	Time to arrival.	81
	al. (2016) [64]	auditory and
		visual.
	M. L. Aust et	Visual and	Response time, Time to collision and	40
	al. (2013) [65]	auditory.	Gaze response time.
	M. Bueno et	Auditory	Brake reaction time, Time to	36
	al. (2014) [66]		collision, Maximum deceleration
			time, Mean deceleration and Task
			load index.
	B. D. Lester	Visual and	Response time and Time to the	15
	(2015) [133]	auditory.	collision.
	J. Son et al.	Auditory.	Average time headway, Average
	(2015) [134]		forward collision warning counts, and
			Percentage of the journey.

Table 2 . 8 :

 28 EEG wave band frequencies.

	EEG Wave	Frequency Band	Mental State
		(Hz)	Representation
	Beta	15-30	Conscious
	Alpha	8-13	Conscious
	Theta	4-8	Unconscious
	Delta	5-4	Unconscious

Table 2 . 9 :

 29 EEG responses of drivers for different warning systems.

	Author/ Year	Warnings Type	Features	EEG Electrodes	Warning Condition
	C.-T. Lin et al.	Auditory.	EEG power	30	Lane departure.
	(2010) [72]		spectrum and
			reaction time.
	C.-T. Lin et al.	Auditory.	EEG power	30	Lane departure.
	(2013) [73]		spectrum and
			reaction time.
	Y.-T. Wang et	Auditory.	EEG power	32	Lane departure
	al. (2014) [74]		spectrum and		and drowsiness.
			reaction time.
	K.-C. Huang et	Auditory.	EEG power	30	Drowsiness.
	al. (2016) [75]		spectrum and
			reaction time.
	C. Berka et al.	Auditory.	EEG power	06	Drowsiness.
	(2005) [76]		spectrum.
	M. Bueno et al.	Auditory.	ERP (CNV, N1,	34	Forward collision.
	(2012) [77]		N2, P3).
	M. Bueno et al.	Auditory.	ERP (CNV, N1,	34	Forward collision.
	(2013) [78]		N2, P3).
	A. Fort et al.	Auditory.	ERP (N1, N2, P3). 34	Brake.
	(2013) [79]
	Z. Khaliliardali	Visual.	ERP (CNV).	64	Go and stop.
	et al. (2012)
	[80]

Table 3 . 1 :

 31 Self-recorded multimedia dataset characteristic.

	Dataset Variables	Characteristics
	Total number of vehicles	150
	Total video frames	26,185
	Video frame rate	25 fps
	Image size	600 x 800
	Video format	AVI
	Total audio frames	26,185
	Audio sampling rate	44,100 Hz
	Audio type	Monocular
	Recorded Audio format	16 bit, .wav format

Table 3 . 2 :

 32 Datasets for vision-based vehicle detection.

	Dataset	Dataset Description	Source of Recording	Number of Frames
	LISA-Dense	Multiple vehicles, dense traffic, daytime,
	[145]	highway, recorded by placing the camera at	Car	1,600
		the front of the car.
	LISA-Urban	Single vehicle, urban scenario, cloudy
	[145]	morning, recorded by placing the camera at	Car	300
		the front of the car.
	LISA-Sunny	Multiple vehicles, medium traffic, daytime,
	[145]	highway, recorded by placing the camera at	Car	300
		the front of the car.
	iROADS-	Multiple vehicles, low traffic, daytime,
	Daylight	highway, recorded by placing the camera at	Car	903
	[146]	the front of the car.
	iROADS-Tunnel [146]	Very low traffic, inside the tunnel, recorded by placing the camera at the front of the car.	Car	307*
	Source-2	Multiple vehicles, medium traffic, daytime,
	[147]	highway, recorded by placing the camera at	Car	960**
		the front of the car.

Note: *There are 307 images in iROADS-Tunnel dataset

[START_REF] Rezaei | Tunnel Set[END_REF]

, **Available at

[START_REF] Choi | Source-2[END_REF]

.

Table 3 . 3 :

 33 Audio datasets characteristic for vehicle classifications.

	Dataset	Data Description	Properties	Number of Vehicles/ Files	Duration (Sec)
	ESC-50 [150]	Multiple sounds under various conditions and different road scenarios.	44,100 /.ogg	160 Files	802.44
	LITIS Rouen[151]	Multiple sounds under various conditions and different road scenarios.	22,050/ 16bit/.wav	3,026	90,780
	The online dataset ESC-50 [150] consists of fifty audio classes. However, in
	this study, only four classes (i.e., airplane, helicopter, train, and engine, respectively)
	are used to validate our technique. The LITIS Rouen [151] online dataset consists of
	nineteen audio classes named as 1) plane, 2) busy street, 3) bus, 4) cafe, 5) car, 6)
	train station hall, 7) kid game hall, 8) market, 9) metro paris, 10) metro rouen, 11)

billiard pool hall, 12) quiet street, 13) student hall, 14) restaurant, 15) pedestrian street, 16) shop, 17) train, 18) high-speed train and 19) tube station respectively. The dataset consists of total 3,026 audio files and every file is thirty seconds long and sampled at 22,050 KHz.

Table 3 . 4 :

 34 Number of features obtained by using SVD for different data balancing techniques.

	Data Balancing	Number of	Data Balancing	Number of
	Technique	Features	Technique	Features
	RUS	228	NCR	317
	CC	279	OSS	316
	CNN	319	TL	281
	ENN	318	ADASYN	274
	ITH	328	ROS	251
	NM-1	349	SMOTE	247
	NM-2	331	SMOTE-B1	246
	NM-3	311	SMOTE-B2	249

FS is the sampling frequency of the audio signal.

			General	Self-recorded and ESC-50 [150]	LITIS Rouen [151]
	1	W1 = D1	FS/2 -FS	22,050-44,100	11,025-22,050
	2	W2 = D2	FS/4-FS/2	11,025-22,050	5,512.5-11,025
	3	W3 = D3	FS/8-FS/4	5,512.5-11,025	2,756.25-5,512.5
	4	W4 = D4	FS/16-FS/8	2,756.25-5,512.5	1,378.13-2,756.25
	5	W5 = D5 FS/32-FS/16	1,378.13-2,756.25	689.06-1,378.13
	5	W6 = A6	0-FS/32	0-1,378.13	0-689.06

Note:

Table 3 . 6 :

 36 Number of features obtained for the different dataset.

	Feature	Number of Features
	Matrix	Self-Recorded and	LITIS Rouen
		ESC-50 [150]	[151]
	E1	882	441
	E2	441	220
	E3	220	110
	E4	110	55
	E5	55	27
	E6	55	27

Table 3 . 7 :

 37 Number of features obtained for the different dataset.

	Feature

Matrix Number of Features Self-Recorded and ESC-50 [150]

			LITIS Rouen
			[151]
	F1	2647	1324
	F2	2206	1103
	F3	1985	993
	F4	1875	938
	F5	1820	910
	F6	1820	910
	F7	10368	5185

Table 3 . 8 :

 38 Number of features obtained by SVD.

	Feature Matrix	Number of Features ESC-50 LITIS Rouen Self-Recorded [150] [151]
	F1	533	410	150
	F2	349	352	90
	F3	185	177	70
	F4	107	122	56
	F5	64	86	32
	F6	63	86	31
	F7	1125	1140	590

Table 3 .

 3 10: K-map values for the first frame of any N th vehicle.

	Ā1 Ā2	Ā1 A2	A1 A2	A1 Ā2
	(00)	(01)	(11)	(10)

Table 3 .

 3 12: K-map values for any next frame of the N th vehicle.

	Ā1 Ā2	Ā1 A2	A1 A2	A1 Ā2
	(00)	(01)	(11)	(10)

Table 3 . 13 :

 313 Sample size for different power values.

	Alpha (α)	Power (%)	Sample Size
	0.05	80	18
	0.05	85	21
	0.05	90	24
	0.05	95	29

Table 3 . 15 :

 315 Verbal warnings given to the motorcyclist.

	Warning	Length
		(Milliseconds)
	Alert Motorcycle	1,200
	Alert Car	1,200
	Alert Van	1,200
	Alert Bus	1,200
	Alert Truck	1,200

Table 4 . 1 :

 41 Confusion matrix for the proposed technique to calculate different parameters.

		Predicted Vehicle	Predicted
			No-Vehicle
	Actual Vehicle	True Positive	False Negative
		(TP)	(FN)
	Actual No-Vehicle	False Positive	True Negative
		(FP)	(TN)

Table 4 . 2 :

 42 Comparison of the proposed technique with the existing state of the art rearview based vehicle detection methods is given in Table4.2 below. The accuracy has been computed for the dataset source-2 by using the equation given below.Also, an average frame rate processing time has been computed, and for this technique is equal to the average number of frames processed in one second as presented in Table4.2. Results of the proposed visual technique.The proposed technique works perfectly for motorcycle applications. The vehicle footprints can easily be identified in the power spectrogram of the data. It can detect the incoming vehicles perfectly from the rear end on single and multiple lanes. Also, it can work equally for all weather conditions and as well as for day/night scenarios.Figures 4.4

		Accuracy	(%)	=	TP	+	TN TP	+ +	FP TN	+	FN		100	(4.3)
	Reference	Dataset			Accuracy (%)	TPR (%)	FDR (%)	Frame rate (fps)
	Proposed	Self-Recorded			NA		94.25	4.96	23.90
	Work	Multimedia Dataset
		LISA-Dense				NA		95.01	5.01	29.04
		LISA-Urban				NA		94.00	6.60	25.06
		LISA-Sunny				NA		97.00	6.03	37.50
		iROADS-Daylight		NA		98.02	3.89	38.50
		iROADS-Tunnel		NA		96.54	5.83	38.50
		Source-2				92.16		91.50	5.97	41.40
	S. Sivaraman.	LISA-Dense				NA		95.00	6.40	NA
	(2010) [145]	LISA-Urban				NA		91.70	25.50	NA
		LISA-Sunny				NA		99.80	8.50	NA
	J. Choi.	Source-1 and			86.63			NA	NA	11.00
	(2012) [147]	Source-2
	R. K. Satzoda.	LISA-Dense				NA		94.50	6.80	15.50
	(2016) [165]	LISA-Sunny				NA		98.00	9.00	25.40
		iROADS-Daylight		NA		99.40	1.50	25.40
		iROADS-Tunnel		NA		96.40	7.80	25.40

NA: Not available

  From Figure4.8, in random undersampling techniques for both feature sets (i.e., the ones without feature reduction and the features acquired by SVD), OSS performs better as compared to the rest of the techniques for most of the classification methods. B2 data balancing technique performs better as compared to the other classifiers. Yet, there are less TPR values as compared to the SVM classifier results, but RF-50 classifier obtained very low FDR values.

	Without feature reduction, SVM, RF-50, and RF-20 classifiers obtained minimum
	FDR values of 6.16%, 6.25%, and 7.94%, respectively. Using the reduced features, Furthermore, RF-50 classifier TPR and FDR values (i.e., 95.14%, and 5.72%)
	RF-50 and RF-20 classifiers obtained 7.38% and 8.39% FDR values respectively.
	However, SVM for the reduced features obtained minimum FDR value of 7.41%
	using the CC data balancing technique.
	For oversampling data balancing techniques without feature reduction,
	minimum FDR values of 6.62%, 5.51% and 6.93% for SVM, RF-50, and for RF-20
	classifiers are achieved by using the SMOTE-B2 technique. For reduced features set
	acquired by SVD, minimum FDR values of 7.10%, 5.72% and 7.65% for SVM, RF-
	50, and RF-20 classifiers are achieved by using the SMOTE-B1, SMOTE-B2 and
	Random Over Sampling techniques, respectively.
	In general, this methodology achieved the highest TPR value (of 96.63%) by
	using oversampling SMOTE-B2 data balancing technique without feature reduction
	for the SVM classifier. The FDR value of 6.62% is obtained with this data balancing
	technique for the SVM classifier. Similarly, RF-50 classifier obtained the second
	highest TPR value of 96.25% and a very high false detection rate of 18.28% using the
	IHT technique with the reduced feature vector.
	The minimum FDR values (of 5.51% and 5.72%) are obtained by using
	SMOTE-B2 data balancing technique with and without feature reduction for the RF-
	50 classifier. The TPR values for RF-50 classifier obtained by using the SMOTE-B2
	for both feature matrices (95.47% and 95.14%) are small as compared to the highest
	value of the SVM classifier (i.e., 96.63%). However, FDR valued obtained by
	SMOTE-B2 for RF-50 classifier for both feature matrices are considerably lower than
	SVM classifier value (6.62%).
	FDR is an important parameter and it provides information about wrong vehicle

[START_REF] Spelta | Smartphonebased vehicle-to-driver/environment interaction system for motorcycles[END_REF]

.58%, 96.25%, and 92.91% are obtained for the SVM, RF-50 and RF-20 classifiers using NM-1, IHT and CC techniques, respectively. For under-sampling technique, detection. The increase in false detection leads to the increase in false alert for the motorcyclists and can decrease the response of the motorcyclists. Therefore, RF-50 classifiers with or without feature reduction using the SMOTE-

Table 4 .

 4 4 below shows the vehicle classification accuracy obtained by using feature matrix F7 for all classes from the online dataset ESC-50.

Table 4 . 4 :

 44 Four classes' detection by using F7 from online dataset ESC-50[START_REF] Piczak | ESC: Dataset for environmental sound classification[END_REF].

	Class Detection (%)	Helicopter Airplane	Train	Engine
	Helicopter	92.48	0.51	1.84	5.17
	Airplane	0.63	98.36	0.52	0.49
	Train	0.87	0.28	98.23	0.62
	Engine	2.75	1.24	0.12	95.89

From Table

4

.4, the maximum audio samples of the airplane are truly classified, followed by the train class. While the lowest true positive rate is achieved for the

Table 4 . 5 :

 45 LITIS Rouen[START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] online dataset confusion matrix for the proposed technique by using feature matrix F7.

		Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class	Class
		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
	Class 1 97.65 0.00	0.34	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.42	1.01	0.58
	Class 2	0.06 85.17 0.55	0.40	0.17	0.59	0.13	0.62	0.26	0.27	0.96	0.87	0.39	0.22	1.16	0.18	1.90	0.49	5.61
	Class 3	0.00	0.12 99.24 0.04	0.00	0.02	0.00	0.03	0.01	0.00	0.01	0.06	0.01	0.00	0.02	0.00	0.16	0.13	0.15
	Class 4	0.00	0.62	0.31 85.53 0.43	0.20	0.18	4.92	2.07	0.61	0.19	0.78	0.29	0.51	0.77	0.38	1.20	0.39	0.62
	Class 5	0.00	0.01	0.01	0.08 99.10 0.04	0.02	0.36	0.11	0.10	0.00	0.01	0.04	0.01	0.02	0.07	0.01	0.00	0.01
	Class 6	0.00	0.25	0.06	0.03	0.00 93.95 0.05	0.25	0.01	0.01	0.06	0.31	0.09	0.11	0.12	0.00	0.94	0.22	3.54
	Class 7	0.00	0.08	0.01	0.07	0.03	0.02 98.38 0.28	0.1	0.03	0.03	0.18	0.03	0.09	0.19	0.05	0.24	0.04	0.15
	Class 8	0.00	0.22	0.07	0.76	0.34	0.07	0.11 94.53 0.38	0.18	0.05	0.40	0.18	0.09	0.46	0.61	0.59	0.20	0.76
	Class 9	0.00	0.21	0.01	0.06	0.03	0.02	0.01	0.12 97.62 0.12	0.01	0.25	0.01	0.03	0.68	0.09	0.64	0.03	0.06
	Class 0.00	0.41	0.00	0.13	0.04	0.00	0.01	0.20	0.50 96.77 0.01	0.12	0.01	0.01	1.25	0.30	0.17	0.02	0.05
	Class 0.00	0.17	0.02	0.10	0.05	0.06	0.01	0.19	0.03	0.05 98.69 0.08	0.00	0.01	0.05	0.01	0.09	0.04	0.35
	Class 0.00	0.74	0.15	0.51	0.05	0.19	0.36	0.80	0.34	0.27	0.08 92.16 0.22	0.28	1.03	0.07	1.15	0.33	1.27
	Class 0.00	0.01	0.01	0.00	0.04	0.00	0.01	0.21	0.00	0.02	0.00	0.01 98.80 0.00	0.10	0.64	0.02	0.03	0.10
	Class 0.00	0.21	0.02	0.37	0.03	0.06	0.05	0.34	0.09	0.11	0.14	0.16	0.09 96.64 0.46	0.03	0.55	0.14	0.51
	Class 0.00	2.02	0.06	0.65	0.16	0.04	0.07	1.06	3.82	1.99	0.03	0.81	0.05	0.01 86.67 0.85	1.47	0.04	0.20
	Class 0.00	0.38	0.05	0.66	0.47	0.10	0.43	2.07	0.36	0.98	0.02	0.57	0.09	0.20	0.48 91.96 0.48	0.09	0.61
	Class 0.00	0.07	0.01	0.03	0.00	0.01	0.01	0.14	0.01	0.02	0.00	0.13	0.01	0.01	0.07	0.00 99.20 0.04	0.24
	Class 0.00	0.04	0.01	0.01	0.00	0.03	0.00	0.01	0.00	0.00	0.02	0.01	0.02	0.00	0.02	0.00	0.11 99.43 0.29
	Class 0.00	0.10	0.02	0.01	0.00	0.06	0.02	0.03	0.00	0.00	0.01	0.04	0.03	0.08	0.01	0.00	0.18	0.07 99.34

Table 4 . 7 :

 47 Normality test of the ERP data.

	Parameters	Components ERP	Conditions Warning	Statistic	df	Sig.
	Latency	N1	NSS	0.974		0.666
			IBS	0.955		0.253
			IVS	0.972		0.624
			PVS	0.944		0.128
		N2	NSS	0.970		0.557
			IBS	0.963		0.384
			IVS	0.938		0.090
			PVS	0.969		0.525
		P3	NSS	0.947		0.156
			IBS	0.974		0.661
			IVS	0.966		0.468
			PVS	0.951		0.198
		N400	NSS	0.970		0.570
			IBS	0.961		0.348
			IVS	0.967		0.470
			PVS	0.955		0.242
	Amplitude	N1	NSS	0.944		0.125
			IBS	0.949		0.172
			IVS	0.970		0.569
			PVS	0.946		0.145
		N2	NSS	0.960		0.328
			IBS	0.963		0.380
			IVS	0.975		0.713
			PVS	0.942		0.111
		P3	NSS	0.974		0.668
			IBS	0.980		0.846
			IVS	0.941		0.105
			PVS	0.957		0.284
		N400	NSS	0.968		0.502
			IBS	0.970		0.552
			IVS	0.937		0.083
			PVS	0.955		0.247

Table 4

 4 .7, the significant values of all ERP components (for all conditions) are greater than 0.05 and it shows that the ERP data is normally distributed. The amplitude and latency of the ERP components were analyzed by using a one-way ANOVA for repeated measures with warning conditions as withinsubject factors by using the SPSS software.A repeated measures ANOVA for four warning conditions with Greenhouse-Geisser correction determined that the mean amplitude and latency of the ERP differed statistically significantly between different warning conditions: F (7.[START_REF] Kobayashi | Crash detection method for motorcycle airbag system with sensors on the front fork[END_REF] 196.66) = 8.26, P<0.000000005.

Table 4 . 8 :

 48 Latency (ms) and amplitude (µV) and of N1, N2 and P3 components with four warning conditions as factors.

	ERP	Conditions Warning	N1	N2	P3	N400
		NSS-IBS	188,174	211,207	341,323	470,461
	Value	NSS-IVS	(0.004) 188,169 (0.001)	(0.4) 211,202 (0.1)	(0.0004) 341,315 (0.000002)	(0.07) 470,453 (0.002)
	(ms)/P Latency	NSS-PVS IBS-IVS IBS-PVS	188,156 (0.000000006) 174,169 (0.34) 174,156 (0.00007)	211,195 (0.005) 207,202 (0.3) 207,195 (0.04)	341,308 ( 0.00001) 323,315 (0.04) 323,308 (0.03)	470,445 (0.00001) 461,453 (0.04) 461,445 (0.003)
		IVS-PVS	169,156	202,195	315,308	453,445
			(0.01)	(0.15)	(0.2)	(0.08)
		NSS-IBS	-8.49,-7.56	-10.65,-9.87	12.93,12.29	-6.21,-5.47
	Amplitude Value (µV)/P	NSS-IVS NSS-PVS IBS-IVS	(0.08) (0.046) -8.49,-6.73 (0.001) -8.49,-5.95 (0.00008) -7.56,-6.73	(0.01) (0.051) -10.65,-9.18 (0.001) -10.65,-8.45 (0.000005) -9.87,-9.18	(0.4) 12.93,11.72 (0.053) 12.93,10.85 (0.0007) 12.29,11.72	(0.08) -6.21,-4.62 (0.001) -6.21,-3.85 (0.0003)

Université Bourgogne Franche-Comté 32, avenue de l'Observatoire

Binary road region detection technique

To find the binary road region, first and foremost six square patches have been extracted from the ROI image. All the selected patches have the same size and their locations are fixed for each ROI image. In our case, these patches are selected near the bottom of the ROI image, which is adjacent to the motorcycle. At that place, there is a low probability of vehicle presence and a high probability that it contains only the road region. The locations of these user-defined patches are shown in Figure 3.4 below.

It should be noted that it may be possible for some of these six patches to contain a white line or lane marker region affecting (i.e., increasing) the mean value of these patches. Therefore, only patches having a mean grey level lower than an empirical threshold are kept, and the affected one(s) is/are discarded for the remaining frames.

The threshold value for the patch selection is given as:

Then, two possible scenarios arise: 1) One or more patches contain(s) a road region, and 2) No patch contains a road region.

If one or more patches contain the road regions, the average grayscale values of these patches are used to calculate the mean value. This mean value is also stored as a PMV and is used to convert that ROI image into a binary image. For example, if M(1), M(2) and M(6) are the average grayscale values of the patches that contain the road region (i.e., M(1), M(2) and M(6) ≤ 100; while M(3), M(4) and M(5) > 100), then MV and PMV are computed as:

If no patch contains a road region (i.e., M(1), M(2), …, M(6) >100), then the PMV from the previous frame is used as the mean value to convert the current ROI image into a binary image. From our observation, the binary road detection technique gives reliable results, even when lane markers are present on the road. • Individual who is under medication.

• Individual with any drug abuse or addiction.

• Individual who has psychological or intellectual problems.

• Individual who goes through a severe accident, which affects his/her driving abilities.

• Individual with on wheel sleep disease. The questionnaire and the flyer (which was used for the advertisement) are given in appendix A and B, respectively.

Based on the above criteria, a total number of 29 subjects (15 male and 14 females) from 21 to 42 years old (mean 29.65 years) were selected for this research.

All of the subjects have valid motorcycle driving license with the minimum 3 years of motorcycle driving experience. None of the subject had experience of the rear end collision warning system. However, they have the experience of the navigation system (which is one type of a driver assistant system).

Safety of Participants and Ethics

Participation in this research was entirely voluntary. It was the participant choice whether to participate or not. Also, there was no drug (or any other effective consumable) usage throughout the experiment. Participants performed the experiment in their normal conditions. The participant was also given an option that he/she could stop at any time he/she wanted. Hence, there is no side effect as a result of this experiment. The experiment protocol and equipment used in this study were safe and non-invasive.

duration of 250 to 400 milliseconds. Finally, the N400 was extracted from EEG electrodes of Fz, F3, and F4 on-time duration of 400 to 500 milliseconds.

These electrode sites were chosen based on the spatial distribution of each component on the scalp (see section 4.2) and were consistent with locations observed in the literature [START_REF] Bueno | An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task[END_REF][START_REF] Bueno | Effectiveness of a Forward Collision Warning System in simple and in dual task from an electrophysiological perspective[END_REF][START_REF] Fort | Impact of totally and partially predictive alert in distracted and undistracted subjects: An event related potential study[END_REF]. The amplitude and latency of the N1, N2, and P3 were also analyzed by using a one-way ANOVA for repeated measures with warning conditions (4×) as within-subject factors.

Our technique achieves a high accuracy for a source-2 dataset with respect to the J. Choi [START_REF] Choi | Realtime on-road vehicle detection with optical flows and Haar-like feature detectors[END_REF] work. For LISA-Dense and LISA-Urban datasets, our method achieves higher true positive rate as compared to the S. Sivaraman [START_REF] Sivaraman | A general active-learning framework for on-road vehicle recognition and tracking[END_REF] and R. K.

Satzoda [START_REF] Satzoda | Multipart vehicle detection using symmetry-derived analysis and active learning[END_REF] recent research works. For LISA-Sunny dataset, our technique provides a true positive rate that is a bit less as compared to the S. Sivaraman [START_REF] Sivaraman | A general active-learning framework for on-road vehicle recognition and tracking[END_REF] and R. K.

Satzoda [START_REF] Satzoda | Multipart vehicle detection using symmetry-derived analysis and active learning[END_REF] techniques. However, the false detection rate of our method for the LISA-Sunny dataset is less compared to the existing work.

For iROADS-Daylight dataset, our technique's true positive rate and false detection rate are lower as compared to the R. K. Satzoda [START_REF] Satzoda | Multipart vehicle detection using symmetry-derived analysis and active learning[END_REF] work, but a higher frame rate is achieved. R. K. Satzoda's [START_REF] Satzoda | Multipart vehicle detection using symmetry-derived analysis and active learning[END_REF] technique performs better under sunlight condition, while our technique performs well for all light conditions and traffic scenarios. The proposed work achieves higher frame rate for the self-recorded dataset as compared to the all existing methods [START_REF] Sivaraman | A general active-learning framework for on-road vehicle recognition and tracking[END_REF][START_REF] Choi | Realtime on-road vehicle detection with optical flows and Haar-like feature detectors[END_REF][START_REF] Satzoda | Multipart vehicle detection using symmetry-derived analysis and active learning[END_REF]. The rapid vehicle detection gives more time to the motorcyclist to take a correct decision, therefore making it more suitable for motorcycle applications.

The results show that our technique works perfectly well for the motorcycle (using the self-recorded dataset) as well as for the online car datasets. It can detect both incoming and outgoing vehicles perfectly from the rear end on single and multiple lanes. Due to the use of low level of features (such as Harris edge detection and the lines computed from Hough transform for vehicle footprint detections), higher computing performance has been obtained. The proposed method achieves a higher accuracy in lesser time, which makes it efficient for motorcycle applications.

Audio-based Vehicle Detection

For Moreover, the TPR values obtained by using the feature matrices F5 and F6 are higher as compared to the TPR obtained by using any other feature matrix for all six classes. The minimum TPR and maximum FDR values are obtained for the feature matrix F1. This is due to the moving vehicle sound signatures appeared in lower frequency components, respectively [START_REF] Tyagi | Vehicular traffic density state estimation based on cumulative road acoustics[END_REF][START_REF] Borkar | Review on vehicular speed, density estimation and classification using acoustic signal[END_REF]. The TPR values obtained for the most related four classes (i.e., helicopter, airplane, train and engine) from ESC-50 [START_REF] Piczak | ESC: Dataset for environmental sound classification[END_REF] are given in Figure 4.12 below.

From Figure 4.12, the maximum classification values are obtained by using the feature matrix F7. The TPR values obtained using feature matrix F7 for helicopter, airplane, train, and engine are 92.48%, 98.36%, 98.23% and 95.89%, respectively.

Using feature matrix F4 the highest TPR value is achieved for the class airplane.

While for all other feature matrices the highest TPR values are obtained for the class train.

helicopter class which is 92.48%. Also, 5.17% of helicopter class are wrongly identified as engine class. Overall, the average detection rate (i.e., TPR value) of 96.23% is achieved by using the feature matrix F7. The given methodology successfully differentiated between all four classes of the ESC-50 dataset. More than 90% of the true positive rate is obtained for all four classes.

The proposed technique has also been investigated for more complex online audio dataset LITIS Rouen [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF]. In this dataset, the most related classes are car, bus, and train, respectively. The TPR values obtained for by using different feature matrices for all classes are shown in Figure 4.13 below.

From Figure 4.13, the maximum true positive rate (i.e., also known as precision in A. Rakotomamonjy and G. Gasso [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] study) is obtained for all nineteen classes by using the feature matrix F7. Next to it, the highest results are obtained for the feature matrices F6 and F5, respectively. The lowest true positive rate is achieved for the feature matrix F1. For all classes, the true positive rate decreases when frequency increases in feature matrices.

The highest results are obtained for the class train (i.e., 99.43%) by using feature matrix F7. For feature matrix F7, the lowest results are obtained for the class busy street at 85.17%. Overall, the lowest results of 54.67% are achieved for the class cafe by using the feature matrix F1. The proposed methodology achieved a higher true positive rate by using feature matrix F7 for sixteen audio classes as compared to the existing state of art method A. Rakotomamonjy and G. Gasso [START_REF] Rakotomamonjy | Histogram of gradients of time-frequency representations for audio scene classification[END_REF] study. The different classes' precisions (i.e., true positive rate) obtained by using feature matrix F7 are given in Table 4.5 below. For vehicle detection and classification, the true positive rate of 95.87% is obtained from the self-recorded dataset. The true positive rate is also higher as compared to both vehicle detection techniques. The false detection rate of 5.25% is obtained by the fusion of techniques. The false detection rate is less as compared to the audio-based vehicle detection method and is slightly higher as compared to the vision-based vehicle detection methodology. However, it provided very useful information about the approaching vehicle (i.e., its type). Therefore, the small increase in the false detection rate can be ignored.

Also, the frame rate has been computed for fusion methodology. The average frame rate obtained is 23.25 fps. The higher frame rate can provide a very fast and reliable vehicle detection/classification warning to the motorcyclist. Therefore, this technique can be very helpful to reduce the motorcyclist accidents. • Also, the proposed methodology successfully detected and classified the vehicles using their acoustic power spectrum obtained by STFT and wavelet transform into five different categories (i.e., motorcycle, car, bus, truck and multiple types, respectively).

Alerts for Detected and Classified Vehicles

• The mean variations in latency and amplitude of ERP components (i.e., N1, N2, P3, and N400, respectively) reveal that the motorcycle rear-end collision warning system with auditory verbal warnings can be helpful for the motorcyclist to avoid the possible rear-end collisions.

Recommendation and Future Work

From this research, it has been found that the collision warning systems can be helpful to save many lives by avoiding the accidents from occurring at first place.

Therefore, the motorcycle manufacturing companies (e.g., Honda, Yamaha, and Suzuki, etc.,) can take this research and perform further analysis of this collision detection system to integrate it with a motorcycle. Also, the traffic police department can make it compulsory for motorcyclists to use collision detection system.

In this research for the motorcycle collision detection system, the vehicles are classified into five different types. In future, these vehicles can be classified into further subcategories based on the vehicle models (e.g., Honda, Toyota, BMW, Mercedes, etc.) by using their acoustic signatures. This collision detection and classification system can also be linked with an ESC system to further increase the stability of the motorcycle and response of motorcyclist can be investigated.

Moreover, the physiological responses of motorcyclist have been investigated

for the rear end collision warning systems by using auditory verbal warnings.

INTRODUCTION

Collision avoidance systems assist the drivers to avoid the accidents. Generally, the collision avoidance systems can detect the approaching vehicles and evaluate their arrival time of an intersection with the vehicles. In case of driver have collision chances, these systems provide warning messages to the drivers. The response of driver against these warning messages is important to study.

This research aims to design a collision avoidance system for motor-bikers and to study the psychological response of motor-bikers against the warning messages generated by this system. For the study of psychological response, we need participants.

As part of our research for psychological response of motor-bikers, we need to find out about the people participating in the study. In particular we need to collect some general information about your lifestyle, health and riding history.

All information you give to us will be CONFIDENTIAL and ANONYMOUS.

Please fill in SECTIONS 1 to 4 before going to the study test session.