Motivated by the desire to explore future traffic flows that will consist of a mixture of classical vehicles and vehicles equipped with advanced driver assistance systems, new mathematical theories and models are developed. The basis for this theory was borrowed from the kinetic description of gas flows, where we replaced the behaviour of the molecules by typical human driving behaviour. From a methodological point of view, this 'human-kinetic' traffic flow theory provides two major improvements with respect to existing theory. Firstly, the model builds exclusively on a mathematical description of individual driver behaviour, whereas traditionally field measurements of traffic flow variables like flow rate and average speed of the flow are needed. This is of major importance for the exploration of future traffic flows with vehicles and equipment that are not yet on the market, and for which at best individual test results from driving simulator experiments or small scale field trials are available. Secondly, the model accounts for the more refined aspects of individual driver behaviour by considering the 'internal' state of the driver (active/passive, aware/unaware,...) and the variations of driving strategy that occur during driving. This is important when the ambition is to capture refined congestion patterns like the occurrence of stop-and-go waves, oscillating congestion and long jams, where the driving strategy may depend for instance on the motivation of the driver to follow closely. This new theory links together the worlds of traffic engineers and behavioural scientists. As such, it is a promising tool that increases the insight in the human behaviour as a basis of various dynamic congestion patterns, and it facilitates the design and evaluation of electronic systems in the vehicle that assist the driver to behave safer, more comfortable and more efficient in busy traffic flows. Herewith, the results of this research are relevant, both for the theoretical interest of the TRAIL research school, and for the more practically oriented work of TNO, who provided financing for this research in the joint T3 research program.

The aging process is often accompanied by declines in one or more physical, vision, and/or cognitive abilities that may impact driving safety. As older drivers become more self-aware of these functional deficits, they have the tendency to engage in self-regulation practices, such as less driving and avoiding challenging driving situations. This tendency may gradually evolve to give up driving altogether. Advanced Driver Assistance Systems (ADAS) holds promise for improving older drivers' safety on the road as well as maintaining their mobility by compensating for declines in visual, cognitive, and physical capabilities. However, the perception of these technologies can influence the realization of these expected benefits. The overarching goal of this research is to understand and enhance the safety and mobility of older adults by examining the impact of ADAS. The dissertation addresses this goal by investigating mobility, perception, safety measures, and safety. Study 1 employed structure equation modeling (SEM) on the data from the Second Strategic Highway Research Program (SHRP 2) on driving habits with respect to age, gender, living status, health, and functioning capabilities. The results illustrate that older drivers' health is a reliable predictor of driving exposure, and cognitive and physical declines are predictive of their intention to reduce exposure and actual driving in challenging situations. These findings highlight that the aging population requires support for their mobility and likely road safety given their age-related impairments. Study 2 employed structure topic modeling on a focus group of older adults driving vehicles equipped with ADAS for six weeks was conducted to reveal five key issues to older drivers (in the order of prevalence): (1) safety, (2) confidence concerning ADAS, (3) ADAS functionality, (4) user interface/usability, and (5) non-ADAS related features. The findings point to a need for holistic ADAS design that not only must consider safety concerns but also user interfaces accommodating older adults' preferences and limitations as well as in-depth training programs to operate ADAS given the technology limitations. Study 3 employed correlation analysis and logistic regression on SHRP 2 data to reveal that the longitudinal deceleration events at greater than 0.60g and lateral acceleration events at greater than 0.40g appear most associated with older adults' driving risk and are predictive of near future crash and near-crashes (CNCs) occurrence and high-risk older drivers with acceptable accuracy. These findings indicate that high g-force events can be used to assess risk for older drivers, and the selection of thresholds should consider the characteristics of drivers. Study 4 compared high g-force events between two naturalistic driving studies to reveal that drivers who drove vehicles equipped with ADAS had lower longitudinal declaration rates, indicating the benefits of ADAS presence on older drivers' safety. When lane keeping assist (LKA) was engaged, lower high longitudinal deceleration was observed than when LKA was not engaged, indicating that older drivers tended to apply less aggressive braking when using LKA. Over several weeks of exposure to vehicles with ADAS presence, older drivers showed decreasing longitudinal deceleration but increasing lateral acceleration events. In other words, the potential of ADAS for positive safety-related impacts exists but some refinement in the design to reduce lateral events might be necessary. / Doctor of Philosophy / As people grow older, they may experience declines in their physical, vision, and cognitive abilities, which can affect their ability to drive safely. Many older drivers become more aware of these limitations and tend to drive less or avoid challenging situations, gradually some eventually stop driving altogether. Advanced Driver Assistance Systems (ADAS) hold the potential to enhance the safety and mobility of older drivers by compensating for these declines in vision, cognition, and physical capabilities. However, the way older adults perceive and accept these technologies can influence their effectiveness. This research focuses on understanding and improving the safety and mobility of older adults by examining the impact of ADAS on them through four studies. These studies fill gaps in research and provide insights into the potential of ADAS to enhance both the safety and mobility of older drivers. This research is vital for improving the quality of life for older adults and making our roads safer for all.

Les systèmes d'aide à la conduite gèrent un grand volume de données qui doivent être mises à jour régulièrement. Cependant, ces systèmes ne permettent, ni de les stocker, ni de les gérer d'une manière efficace. Pour ces raisons, nous proposons l'intégration d'un système de bases de données temps réel (TR) dans les systèmes d'aide à la conduite. Cela permet d'améliorer la tolérance aux fautes, de réduire le nombre de transactions et de réduire leur temps de réponse. La gestion d'un grand volume de données et leurs contraintes TR rend ces systèmes plus complexes, ce qui rend leur modélisation plus difficile. Pour remédier à cette complexité, nous avons proposé trois patrons de conception en nous basant sur un processus de création de patrons. Ce processus permet de définir les étapes à suivre pour déterminer les fonctionnalités et les exigences du domaine d'aide à la conduite, d'une part, et de définir les règles d'unification pour générer les diagrammes UML de classes et de séquence, d'autre part. Pour représenter ces patrons, nous avons proposé le profil UML-RTDB2, pour tenir compte : (i) de l'expression de la variabilité des patrons, (ii) de la représentation des contraintes TR et des aspects non fonctionnels et (iii) des éléments instanciés à partir des patrons lors de la modélisation d'une application cible. Une fois les patrons créés, ils peuvent être réutilisés par les concepteurs pour modéliser des systèmes spécifiques. Pour cela, nous avons proposé un processus de réutilisation pour guider les concepteurs d'applications lors de la réutilisation des solutions de patrons. Enfin, nous avons procédé à l'évaluation de ces patrons en utilisant deux catégories de métriques. / Advanced Driver Assistance Systems (ADAS) manage an important volume of data that must be updated regularly. However, ADAS don't store, nor manage efficiently these data. For these reasons, we propose to integrate a real-time (RT) database system into ADAS. The integration of the RT database system allows improving the fault tolerance, reducing the number of transactions and minimizing their response time. The management of a lot of data makes these systems complex, thus, their design is highly difficult. To tackle this problem, we have proposed three patterns based on the pattern development process. This process allows defining the steps to follow in order to determine the functionalities and the requirements of the driver assistance domain on one hand, and defining the unification rules for the generation of the UML class and sequence diagrams, on the other hand. In order to represent these patterns, we have proposed UML-RTDB2 profile, which allows (i) expressing the variability of patterns, (ii) representing the real time constraints and the non functional properties and (iii) identifying the role played by each pattern element in a pattern instance. Once the proposed patterns are created, they can be reused by designers to model a specific application. For this reason, we have proposed a process to assist the applications designers when instantiating the patterns solutions. Finally, we have evaluated these patterns based on two categories of metrics.

Vehicle automation has garnered increasing attention as a means of improving safety and efficiency. Advanced Driver Assistance Systems (ADAS) have gained popularity in the transport industry. However, establishing an appropriate level of trust in these systems is crucial for their successful implementation. This research explores the factors influencing driver trust calibration in different levels of automation within driver assistance systems for commercial mobility trucks to ensure drivers comprehend the limitations of these systems and uphold road safety. A qualitative approach involved eleven interviews and observations with drivers to explore their perceptions, experiences, and expectations regarding these systems. The study’s findings extend the Hoff and Bashir Trust model to include significant social factors in calibrating trust. These findings offer valuable insights into the various trust factors that impact driver trust calibration at different levels of automation in driver assistance systems for commercial mobility trucks. These insights contribute to academia in that they help understand how trust in automation is formed and calibrated in real-world settings. In the automotive industry, they can guide the design and implementation of these systems to enhance future drivers’ safety and overall experience.

Advanced Driver Assistance Systems (ADAS) and semi-autonomous driving systems are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., are well accepted and endorsed by transportation safety researchers and federal transportation agencies. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of ADAS and semi-autonomous driving systems into the transportation system. These technologies will not achieve their potential if drivers do not accept them and use them in a sustainable and appropriate manner. The potential benefits of these in-vehicle assistive systems presents a strong need for research. A comprehensive review of current literature on the definitions of acceptance, acceptance modelling approaches, and assessment techniques was carried out to explore and summarize the different approaches adopted by previous researchers. The review identified three major research needs: a comprehensive evaluation of general technology acceptance models in the context of ADAS, development of an acceptance model specifically for ADAS and similar technologies, and development of an acceptance assessment questionnaire. Two studies were conducted to address these needs. In the first study, data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants were exposed to an ADAS and, based on their experience, responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, reliability, etc. The results of the first study showed the utility of the general technology acceptance theories to model driver acceptance. A Unified Model of Driver Acceptance (UMDA) and two versions (a long version with 21 items and a short version with 13 items) of an acceptance assessment questionnaire were also developed, based on the results of the first study. The second was conducted to validate the findings of first study. The results of the second study found statistical evidence validating UMDA and the two versions of the acceptance assessment questionnaire.

A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.

More than 900 bicyclists died in motor vehicle crashes in the United States in 2020, which represents a 50% increase from 2010 and the highest number of bicyclist deaths in nearly 35 years [1]. Reversing this trend will require efforts on multiple fronts, including reducing vehicle speeds and improving roadways and vehicles to be more hospitable to cyclists. Automatic emergency braking (ABB) with cyclist detection is a vehicle countermeasure with potential to prevent bicycle-motor vehicle crashes. AEB systems, which typically warn drivers of an impending collision and brake if drivers do not respond, have been shown to reduce vehicle-to-vehicle rear-end crash rates by 50% [2] and pedestrian crash rates by 27% [3]. Little is known about the real-world effects of ABB with cyclist detection on bicycle crashes. Subaru's EyeSight system, which includes ABB, has been capable of detecting cyclists in parallel configurations beginning in model year (MY) 2013 in the United States. The ability to detect cyclists in perpendicular configurations was added to some models beginning in MY 2022. The goal of this study is to evaluate the effects of the early version of EyeSight on U.S. bicycle crashes. [from Introduction]

Annually, road accidents cause more than 1.2 million deaths, 50 million injuries, and US$ 518 billion of economic cost globally. About 90% of the accidents occur due to human errors such as bad awareness, distraction, drowsiness, low training, fatigue etc. These human errors can be minimized by using advanced driver assistance system (ADAS) which actively monitors the driving environment and alerts a driver to the forthcoming danger, for example adaptive cruise control, blind spot detection, parking assistance, forward collision warning, lane departure warning, driver drowsiness detection, and traffic sign recognition etc. Unfortunately, these systems are provided only with modern luxury cars because they are very expensive due to numerous sensors employed. Therefore, camera-based ADAS are being seen as an alternative because a camera has much lower cost, higher availability, can be used for multiple applications and ability to integrate with other systems. Aiming at developing a camera-based ADAS, we have performed an ethnographic study of drivers in order to find what information about the surroundings could be helpful for drivers to avoid accidents. Our study shows that information on speed, distance, relative position, direction, and size & type of the nearby vehicles & other objects would be useful for drivers, and sufficient for implementing most of the ADAS functions. After considering available technologies such as radar, sonar, lidar, GPS, and video-based analysis, we conclude that video-based analysis is the fittest technology that provides all the essential support required for implementing ADAS functions at very low cost. Finally, we have proposed a Smart-Dashboard system that puts technologies – such as camera, digital image processor, and thin display – into a smart system to offer all advanced driver assistance functions. A basic prototype, demonstrating three functions only, is implemented in order to show that a full-fledged camera-based ADAS can be implemented using MATLAB. / Phone# 00966-56-00-56-471

A segurança no tráfego de carros é um assunto em foco nos dias de hoje e, dentro dele, podem-se citar os sistemas de auxílio ao motorista que vêm sendo desenvolvidos com a finalidade de reduzir o grande número de fatalidades em acidentes de trânsito. Tais sistemas de auxílio buscam mitigar falhas humanas como falta de atenção e imprudência. Visto isso, o projeto SENA, desenvolvido pelo Laboratório de Robótica Móvel da Escola de Engenharia de São Carlos, busca contribuir com a evolução dessa assistência ao motorista. O presente trabalho realiza um estudo sobre uma técnica de inteligência artificial chamada de Redes Bayesianas. Essa técnica merece atenção em virtude de sua capacidade de tratar dados incertos em forma de probabilidades. A rede desenvolvida por esse trabalho utiliza, como dados de entrada, os classificadores em desenvolvimento no projeto SENA e tem como resposta um comportamento que o veículo deve executar, por um ser humano ou por um planejador de trajetórias. Em função da alta dimensionalidade do problema abordado, foram realizados dois experimentos em ambiente simulado de duas situações distintas. A primeira, um teste de frenagem próximo a um ponto de intersecção e a segunda, um cenário de entroncamento. Os testes feitos com a rede indicam que classificadores pouco discriminantes deixam o sistema mais propenso a erros e que erros na localização do ego-veículo afetam mais o sistema se comparado a erros na localização dos outros veículos. Os experimentos realizados mostram a necessidade de um sistema de tempo real e um hardware mais adequado para tratar as informações mais rapidamente / The safety of cars in traffic scenarios is being addressed on the past few years. One of its topics is the Advanced Driver-Assistance Systems which have been developed to reduce the fatality numbers of traffic accidents. These systems try to decrease human failures, such as imprudence and lack of attention while driving. For these reasons, the SENA project, in progress on the Mobile Robotics Laboratory at the Sao Carlos School of Engineering (EESC), aims to contribute for the evolution of these assistance systems. This work studies an artificial intelligence technique called Bayesian Networks. It deserves our attention due to its capability of handling uncertainties with probability distributions. The network developed in this Masters Thesis has, as input, the result of the classifiers used on SENA project and has, as output, a behavior which has to be performed by the vehicle with a driver or autonomously by the means of a path planner. Due to the high dimensionality of this issue, two different tests have been carried out. The first one was a braking experiment near a intersection point and the other one was a T-junction scenario. The tests made indicate that weak classifiers leaves the system more instable and error-prone and localization errors of the ego-vehicle have a stronger effect than just localization errors of other traffic participants. The experiments have shown that there is a necessity for a real-time system and a hardware more suitable to deal quickly with the information

Le mouvement en essaim est défini par l'action d'un ensemble d'individusautopropulsés se déplaçant en groupe uniquement à l’aide de la connaissance locale de leur environnement.L'objectif scientifique de la thèse consiste à mettre en oeuvre ce type demodèle de comportement appliqué à un flot de véhicules se déplaçant sur un profilroutier, et ce afin d'assister le conducteur dans ses actions à la fois pour son confortet sa sécurité.A partir de l’analyse d’une synthèse bibliographique, une stratégie dehiérarchisation a été mise en place afin de créer un système d’aide à la conduite ouADAS (de l’anglais « Advanced Driver Assistance System »). Ainsi, dans un premiertemps, il s’agit de générer une trajectoire à partir de ce type de modèle qui respecteles contraintes autoroutières. Ensuite, la dynamique du véhicule est prise en compteafin de transmettre au conducteur via une régulation de vitesse et un retour haptiqueau volant, les deux étant basés notamment sur la commande CRONE, lesmanoeuvres nécessaires au suivi de cette trajectoire. Enfin, le système d’aide à laconduite est mis en oeuvre, non seulement sur un simulateur dynamique de conduiteafin de recueillir le ressenti des conducteur, mais aussi au sein d’un logiciel desimulation de trafic pour évaluer les gains obtenus dans le cas d’un ensemble devéhicules équipés. / Swarm behavior refers to individuals travelling in a group and using only localknowledge of their environment.The scientific objective of the thesis is to implement this type of behaviormodel to vehicles traveling on road, in order to assist the driver in his actions for bothits comfort and security.From a literature review, a prioritization strategy was set up to create anAdvanced Driver Assistance System (ADAS). At first, it is to generate a path from thistype of model that respects the motorway constraints. Then, vehicle dynamics istaken into account in order to transmit to the driver through cruise control and hapticfeedback steering wheel, both based on the CRONE control, maneuvers needed tofollow this trajectory. Finally, the driver assistance system is not only implemented ona dynamic driving simulator to gather driver’s feelings but it is also implemented intraffic simulation software to evaluate gains obtained for a set of equipped vehicles.