This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads.

A travers des systèmes d’assistance à la conduite, l’automatisation de la conduite est introduite graduellement, avec le but de créer un transport plus sûr, confortable et moins polluant. Cette thèse discute le développement d’un système d’assistance à la conduite qui permet une conduite automatisée sur autoroute. La thèse présente le concept « Legal Safety », qui base le développement d’un système d’assistance à la conduite sur le code de la route international. Ceci permet de partager la route avec des conducteurs humains, sans nécessairement changer l’équipement sur l’infrastructure ou sur les autres véhicules. Le « Legal Safety » permet aussi un partage intuitif avec le conducteur du véhicule égo. Chapitre 1 situe le concept « Legal Safety » dans les concepts des systèmes d’assistance à la conduite existants, et discute la méthodologie de recherche de la thèse. Chapitre 2 présente les spécifications sur les composants de perception, contrôle et IHM et compare ces spécifications avec l’état de l’art de ces composants. Chapitre 3 propose le développement d’un composant de calculation de trajectories pour une conduite sur autoroute et discute la contribution de la thèse par rapport l’état de l’art. Chapitre 4 présente le développement du système sur les véhicules et simulateurs du laboratoire LIVIC et des projets HAVEit et ABV. Les différentes implémentations sur PC et sur ECU sont discutées. Chapitre 5 discute les contributions de la thèse. Ce chapitre conclue que le « Legal Safety » pour les composants décision, contrôle et IHM serait possible avec la technologie état de l’art. Une perception selon le « Legal Safety » pourrait être développée en moyen terme. / Vehicle automation is proposed as one of the solutions to make transport safer, more comfortable and more environmentally friendly. It is gradually being introduced through Advanced Driver Assistance Systems (ADAS). This work aims to contribute to this evolution, by discussing how driving systems can share the road with human drivers. It presents the legal safety concept for the design of a highly automated driving system for highways. The legal safety concept proposes to base driving system design on traffic rules. This allows fully automated driving in traffic with human drivers, without necessarily changing equipment on other vehicles or infrastructure. The driving system can interact with the human driver, via human rules. If needed, the driving system takes over control in order to avoid accidents. With the third set of rules of the legal safety concept, system rules, system components respect the limitations of other system components. The requirements on PERCEPTION, control and Human-Machine Interface (HMI) components of the legal safety system are discussed. The decision component, which is the central component of the legal safety system, is completely worked out from requirements to design. The legal safety system has been implemented on PC and automotive Electronic Control Units (ECUs). The integration and validation of legal safety components on LIVIC, HAVEit and ABV demonstrators are presented. The work concludes that, for highway environments, legal safety decision, control and HMI can be achieved with state-of-the-art technology, and legal safety perception could be available in medium term.

No description available.

This study examined how decision-making training related to the Drug Evaluation and Classification (DEC) Program was transferred to law enforcement officers, referred to as drug recognition experts (DRE), for use in identifying and assessing impaired drivers. Specifically, this study explored how particular factors observed as part of the DEC Program’s decision-making process influence the DRE’s prediction of a drug category that was impairing a suspected impaired driver in the enforcement environment. Quantitative and qualitative methods were utilized to better understand the complexity of the DRE’s decision-making. Factors observed from 199 drug influence evaluations (DIE) were used as a basis for the quantitative analysis. In addition, feedback gleaned from the interviews conducted with six DREs was analyzed to identify themes that described the perceptive influence of those same factors on the DRE’s prediction of a drug category. The DREs classified 88.4% of the DIEs correctly when compared to the toxicology results according to the criteria set-forth in the DEC Program’s Administrator’s Guide. The accuracy rates at the drug category level were 82.9% for Depressants and Cannabis, 80.9% for Stimulants, 96.5% for Dissociative Anesthetics, and 81.9% for Narcotic Analgesics. The results of the study showed that the DREs employed their DEC Program training appropriately, but reportedly used a subset of factors as a basis for their predictions. The quantitative analysis indicated that the factors the DRE expected to observe when a particular drug category was present in the toxicology results were documented as present on the DIE report by the DRE. In contrast, only a subset of those factors was unique to that drug category. The qualitative feedback from the DREs indicated that they rely on a subgroup of factors, such as those related to the eyes, as the main basis for their decision-making. The DREs also emphasized their consideration of the totality of evidence as major driver in their decision-making. The DEC Program provided an interesting opportunity to explore the transfer of decision-making training. Based on the results of this study, the DEC Program can improve the transfer of training by targeting DRE’s motivation to transfer training into practice, the transfer design, and the climate in which the DRE transfers their learning into performance.

A novel gate driver design is proposed to improve the conversion efficiency of DC-DC converters. Conventional gate drivers provide a fixed gate driving strength (capability) over the entire output load range. However, it is demonstrated that to optimize the overall conversion efficiency, the driving capability of the gate driver circuit should be adjusted according to the loading condition. The proposed segmented gate driver consists of 8 parallel driver segments that can be turned on/off allowing the power consumption of the gate driver circuit to be dynamically adjusted. The post layout simulation results in high voltage TSMC 0.25µm CMOS process shows that up to 7% improvement in the efficiency can be achieved. Furthermore, in addition to efficiency improvements, a 60% reduction in the ringing and overshoot/undershoot was observed. An integrated segmented gate driver IC designed for AMSP35HV process was submitted for fabrication with the support from CMC.

Driving is a cognitively complex task, yet the areas of the brain involved in driving are not well understood. This thesis investigates the neural correlates of driving using functional magnetic resonance imaging and driving simulations with custom built driving hardware (steering wheel and foot pedals). The feasibility of driving in fMRI is first investigated, and a methodology is described to minimize head motions. Next, the functional neuroanatomical correlates of driving tasks of varying complexity are explored. Simple tasks such as straight driving activate areas of the brain related to motion, spatial navigation and coordination. Complex tasks are found to recruit additional areas of the brain, including areas of higher cognitive function such as the prefrontal cortex. Developing a better understanding of the areas involved in different driving tasks is an important first step in understanding the neural basis of driving skill and safe driving.

While most seniors are capable, conscientious drivers, some have experienced age-related declines in the skills that support safe driving, thus pose a hazard to themselves and to other road users. There seems to be agreement that older adults should stop driving when their skills have declined to the point that they pose a risk to themselves and others, but there are few guidelines to aid older drivers or their families in determining when one should no longer drive. This study was designed to identify driving behaviors and non-driving measures that predict hazardous driving errors such as leaving the roadway or hitting pedestrians other cars. Ten younger and 30 older adults participated in the study; older participants were divided into three groups of 10 based on performance on cognitive screening tests. Participants completed tests of attention, working memory, spatial memory and timing ability as well as simulated driving scenarios. The younger participants made significantly fewer hazardous errors than did the older drivers with poorer cognitive performance. Driving behaviors associated with increased hazardous errors in older participants included poor maintenance of lane position in low complexity condition and making abrupt lane changes when complexity increased. Poor performance on a measure of working memory and on an anticipation timing task were also associated with increased hazardous errors in the older participants. / Thesis (Ph.D.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Psychology / "December 2005."

A novel gate driver design is proposed to improve the conversion efficiency of DC-DC converters. Conventional gate drivers provide a fixed gate driving strength (capability) over the entire output load range. However, it is demonstrated that to optimize the overall conversion efficiency, the driving capability of the gate driver circuit should be adjusted according to the loading condition. The proposed segmented gate driver consists of 8 parallel driver segments that can be turned on/off allowing the power consumption of the gate driver circuit to be dynamically adjusted. The post layout simulation results in high voltage TSMC 0.25µm CMOS process shows that up to 7% improvement in the efficiency can be achieved. Furthermore, in addition to efficiency improvements, a 60% reduction in the ringing and overshoot/undershoot was observed. An integrated segmented gate driver IC designed for AMSP35HV process was submitted for fabrication with the support from CMC.

Driving is a cognitively complex task, yet the areas of the brain involved in driving are not well understood. This thesis investigates the neural correlates of driving using functional magnetic resonance imaging and driving simulations with custom built driving hardware (steering wheel and foot pedals). The feasibility of driving in fMRI is first investigated, and a methodology is described to minimize head motions. Next, the functional neuroanatomical correlates of driving tasks of varying complexity are explored. Simple tasks such as straight driving activate areas of the brain related to motion, spatial navigation and coordination. Complex tasks are found to recruit additional areas of the brain, including areas of higher cognitive function such as the prefrontal cortex. Developing a better understanding of the areas involved in different driving tasks is an important first step in understanding the neural basis of driving skill and safe driving.

This study examined how decision-making training related to the Drug Evaluation and Classification (DEC) Program was transferred to law enforcement officers, referred to as drug recognition experts (DRE), for use in identifying and assessing impaired drivers. Specifically, this study explored how particular factors observed as part of the DEC Program’s decision-making process influence the DRE’s prediction of a drug category that was impairing a suspected impaired driver in the enforcement environment. Quantitative and qualitative methods were utilized to better understand the complexity of the DRE’s decision-making. Factors observed from 199 drug influence evaluations (DIE) were used as a basis for the quantitative analysis. In addition, feedback gleaned from the interviews conducted with six DREs was analyzed to identify themes that described the perceptive influence of those same factors on the DRE’s prediction of a drug category. The DREs classified 88.4% of the DIEs correctly when compared to the toxicology results according to the criteria set-forth in the DEC Program’s Administrator’s Guide. The accuracy rates at the drug category level were 82.9% for Depressants and Cannabis, 80.9% for Stimulants, 96.5% for Dissociative Anesthetics, and 81.9% for Narcotic Analgesics. The results of the study showed that the DREs employed their DEC Program training appropriately, but reportedly used a subset of factors as a basis for their predictions. The quantitative analysis indicated that the factors the DRE expected to observe when a particular drug category was present in the toxicology results were documented as present on the DIE report by the DRE. In contrast, only a subset of those factors was unique to that drug category. The qualitative feedback from the DREs indicated that they rely on a subgroup of factors, such as those related to the eyes, as the main basis for their decision-making. The DREs also emphasized their consideration of the totality of evidence as major driver in their decision-making. The DEC Program provided an interesting opportunity to explore the transfer of decision-making training. Based on the results of this study, the DEC Program can improve the transfer of training by targeting DRE’s motivation to transfer training into practice, the transfer design, and the climate in which the DRE transfers their learning into performance.