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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.

This work provides an in-depth-view of driver motivational aspects when driver assistance Systems (DAS) are considered. Thereby, the role of driver actual experience with DAS use was also identified and highlighted. A central outcome of this thesis is the STADIUM model describing the interplay of motivational factors that determine the engagement in secondary activities while taking actual DAS use experience into account. The role of motives in showing attentive behaviour depending on DAS (the navigation system) could also be underlined. The relevance, enrichment and need of combining qualitative and quantitative approaches when the effects of safety countermeasures on driver behaviour are investigated could also be shown. The results are discussed in terms of hierarchical driver behaviour models, the theory of planned behaviour and its extended versions and the strengths of the introduced studies and limitations. Implications for traffic safety are provided and future research issues are recommended. / Diese Arbeit liefert einen gründlichen Einblick, welche Rolle motivationale Aspekte spielen, wenn Fahrerassistenzsysteme (FAS) genutzt werden. Dabei wurde auch die Funktion der tatsächlichen Erfahrung mit FAS identifiziert und hervorgehoben. Ein zentrales Ergebnis dieser Arbeit ist das STADIUM Modell, welches das Zusammenspiel motivationaler Faktoren in Abhängigkeit von der tatsächlichen Erfahrung mit FAS erklärt, die wiederum bestimmen, inwieweit und ob andere Aktivitäten während des Fahrens ausgeführt werden. Außerdem konnte unterstrichen werden, welche Rolle Motive spielen, aufmerksames Verhalten in Abhängigkeit von der Nutzung von FAS (dem Navigationssystem) zu zeigen. Zusätzlich konnte dargestellt werden, wie relevant, bereichernd und nützlich es ist, qualitative und quantitative Methoden zu kombinieren, wenn die Effekte von FAS auf das FahrerInnenverhalten untersucht werden. Die Ergebnisse werden diskutiert indem auf hierarchische Fahrerverhaltensmodelle, auf die Theorie des geplanten Verhaltens und ihre erweiterten Versionen und auf die Stärken und Schwächen der Studien Bezug genommen wird. Es werden Implikationen dargestellt und zukünftige Forschungsfragen und Problemstellungen empfohlen.

There is a substantial amount of evidence that suggests that driver drowsiness plays a significant role in road accidents. Alarming recent statistics are raising the interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face detection, human skin color detection and eye state classification in a novel way. It follows a behavioral methodology by performing a non-invasive monitoring of external cues describing a driver's level of drowsiness. We look at this complex problem from a systems engineering point of view in order to go from a proof-of-concept prototype to a stable software framework. Our system utilizes two detection and analysis methods: (i) face detection with eye region extrapolation and (ii) eye state classification. Additionally, we use two confirmation processes - one based on custom skin color detection, the other based on nod detection - to make the system more robust and resilient while not sacrificing speed significantly. The system was designed to be dynamic and adaptable to conform to the current conditions and hardware capabilities. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Originally presented as the author's Thesis--Zugl.: Technische Universität München, 2007. / Includes bibliographical references.

This thesis is about the development of a function that assists the driver of a heavy vehicle to do an estimation over the possibilities to overtake a preceding heavy vehicle. The function utilizes Look-Ahead and vehicle-to-vehicle communication to do a calculation of the distance between the vehicles in some road distance ahead. Consequently the report also contains an investigation of what data that is needed to be known about a vehicle to be able to do a satisfying estimation about this vehicle. The most vital problem is to estimate what velocity the vehicle will get in an uphill/downhill slope. A Simulink model is developed to simulate the function with two independent vehicles. Real tests are also performed to evaluate the velocity estimation part of the function.

The goal of this thesis is to develop a smartphone application for augmented reality view; it is an initial attempt to realize a driver assistance functionality using just a smartphone and an external lens. Initially it depicts a brief analysis about the most feasible development technologies for mobile application development, selecting a proper lens and positioning of the smartphone in the car. Later, it discusses the strategies for real-time object detection using OpenCV; the video frames are processed using the strategies to find patterns in the videos. Different techniques like Hough-line transform, watershed, contour detection, color segmentation, color thresholding and HAAR cascades are implemented and compared in terms of real time detection of the desired objects. Then a unified algorithm is implemented for the given scenario which overcomes the challenges faced during the conceptualization phase. Finally, the results are depicted with the snapshots of the real time detection done from the smartphone and then evaluated against the vision of the application and the achieved tasks. This thesis is concluded by stating the prospects of this mobile application in the future.

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.

Objective: With the overall goal to harmonize prospective effectiveness assessment of active safety systems, the specific objective of this study is to identify and evaluate sources of variation in virtual precrash simulations and to suggest topics for harmonization resulting in increased comparability and thus trustworthiness of virtual simulation-based prospective effectiveness assessment. Methods: A round-robin assessment of the effectiveness of advanced driver assistance systems was performed using an array of state-of-the-art virtual simulation tools on a set of standard test cases. The results were analyzed to examine reasons for deviations in order to identify and assess aspects that need to be harmonized and standardized. Deviations between results calculated by independent engineering teams using their own tools should be minimized if the research question is precisely formulated regarding input data, models, and postprocessing steps. Results: Two groups of sources of variations were identified; one group (mostly related to the implementation of the system under test) can be eliminated by using a more accurately formulated research question, whereas the other group highlights further harmonization needs because it addresses specific differences in simulation tool setups. Time-to-collision calculations, vehicle dynamics, especially braking behavior, and hit-point position specification were found to be the main sources of variation. Conclusions: The study identified variations that can arise from the use of different simulation setups in assessment of the effectiveness of active safety systems. The research presented is a first of its kind and provides significant input to the overall goal of harmonization by identifying specific items for standardization. Future activities aim at further specification of methods for prospective assessments of the effectiveness of active safety, which will enhance comparability and trustworthiness in this kind of studies and thus contribute to increased traffic safety.