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.

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.

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.

<p>Models based on micro-simulation of traffic flows have proven to be useful tools in the study of various traffic systems. Today, there is a wealth of traffic microsimulation models developed for freeway and urban street networks. The road mileage is however in many countries dominated by rural highways. Hence, there is a need for rural road traffic simulation models capable of assessing the performance of such road environments. This thesis introduces a versatile traffic micro-simulation model for the rural roads of today and of the future. The developed model system considers all common types of rural roads including effects of intersections and roundabouts on the main road traffic. The model is calibrated and validated through a simulation study comparing a two-lane highway to rural road designs with separated oncoming traffic lanes. A good general agreement between the simulation results and the field data is established.</p><p>The interest in road safety and the environmental impact of traffic is growing. Recent research has indicated that traffic simulation can be of use in these areas as well as in traditional capacity and level-of-service studies. In the road safety area more attention is turning towards active safety improving countermeasures designed to improve road safety by reducing the number of driver errors and the accident risks. One important example is Advanced Driver Assistance Systems (ADAS). The potential to use traffic simulation to evaluate the road safety effects of ADAS is investigated in the last part of this thesis. A car-following model for simulation of traffic including ADAS-equipped vehicles is proposed and the developed simulation framework is used to study important properties of a traffic simulation model to be used for safety evaluation of ADAS. Driver behavior for ADAS-equipped vehicles has usually not been considered in simulation studies including ADAS-equipped vehicles. The work in this thesis does however indicate that modeling of the behavior of drivers in ADAS-equipped vehicles is essential for reliable conclusions on the road safety effects of ADAS.</p> / Report code: LiU-Tek-Lic-2005:60.

Navigation systems are one of the most commonly found electronic gadgets in modern vehicles nowadays. Alongside navigation units this technology is made readily available to individuals in everyday devices such as a mobile phone. Digital maps which come preloaded on these devices accommodate within them an extensive dataset of spatial information from around the globe which aids the driver in achieving a well guided driving experience. Apart from being essential for navigation this sensor information backs up other vehicular applications in making intelligent decisions. The quality of this information delivered is in direct relation to the underlying dataset used to produce these maps. Since we live in a highly dynamic environment with constantly changing geography, an effort is necessary to keep these maps updated with the most up to date information as frequently as possible. The digital map of interest in this study is OpenStreetMap, the underlying data of which is a combination of donated as well as crowdsourced information from the last 10 years. This extensive dataset helps in building of a detailed digital map of the world using well defined cartographic techniques. The information within OpenStreetMap is currently enhanced by a large group of volunteers who willing use donated satellite imagery, uploaded GPS tracks, field surveys etc. to correct and collect necessary data for a region of interest. Though this method helps in improving and increasing the quality and quantity of the OpenStreetMap dataset, it is very time consuming and requires a great deal of human effort. Through this thesis an effort is made to automatically enrich this dataset by preprocessing crowdsourced sensor data collected from the navigation system and driver assistance systems (Traffic Sign Recognition system and a Lane Detection System) of a driving vehicle. The kind of data that is algorithmically derived includes the calculation of the curvature of the underlying road, correction of speed limit values for individual road segments being driven and the identification of change in the geometry of existing roads due to closure of old ones or addition of new ones in the Nuremberg region of Bavaria, Germany. Except for a small percentage of speed limit information on roads segments, other information is currently not available in the OpenStreetMap database for use in safety and comfort related applications. The navigation system has the ability to deliver geographical data in form of GPS coordinates at a certain frequency. This set of GPS coordinates can grouped together to form a GPS track visualizing the actual path traversed by a driving vehicle. A large number of such GPS tracks repeatedly collected from different vehicles driving in a region of interest gives all GPS points which lie on a particular road. These points, after outlier elimination methods are used as a dataset to scientifically determine the underlying curvature of the road with the aid of curve fitting techniques. Additional information received from the lane detection system helps identify curves on a road for which the curvature must be calculated. The fusion of information from these sources helps to achieve curvature results with high accuracy. Traffic sign recognition system helps detect traffic signs while driving, the fusion of this data with geographical information from the navigation system at the instance of detection helps determine road segments for which the recognized speed limit values are valid. This thesis successfully demonstrates a method to automatically enrich OpenStreetMap data by crowdsourcing raw sensor data from multiple vehicles equipped with driver assistance systems. All OpenStreetMap attributes were 100% updated into the database and the results have proven the effectiveness our system architecture. The positive results obtained in combination with minimal errors promise a better future for assisted driving.

The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.