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.

It is the period of the World's history, where the technological progress reached a level that enables the first steps towards the development of vehicles with automated driving capabilities. The swift response from the significant portion of the industry resulted in a race, the final line set at the introduction of vehicles with full automated driving capabilities. Vehicles with automated driving capabilities target making driving safer, more comfortable, and economically more efficient by assisting the driver or by taking responsibilities for different driving tasks. While vehicles with assistance and partial automation capabilities are already in series production, the ultimate goal is in the introduction of vehicles with full automated driving capabilities. Reaching this level of automation will require shifting all responsibilities, including the responsibility for the overall vehicle safety, from the human to the computer-based system responsible for the automated driving functionality (i.e., the Automated Driving System (ADS)). Such a shift makes the ADS highly safe-critical, requiring a safety level comparable to an aircraft system. It is paramount to understand that ensuring such a level of safety is a complex interdisciplinary challenge. Traditional approaches for ensuring safety require the use of fault-tolerance techniques that are unproven when it comes to the automated driving domain. Moreover, existing safety assurance methods (e.g., ISO 26262) suffer from requirements incompleteness in the automated driving context. The use of artificial intelligence-based components in the ADS further complicate the matter due to their non-deterministic behavior. At present, there is no single straightforward solution for these challenges. Instead, the consensus of cross-domain experts is to use a set of complementary safety methods that together are sufficient to ensure the required level of safety. In the context of that, runtime monitors that verify the safe operation of the ADS during execution, are a promising complementary approach for ensuring safety. However, to develop a runtime monitoring solution for ADS, one has to handle a wide range of challenges. On a conceptual level, the complex and opaque technology used in ADS often make researchers ask the question ``how should ADS be verified in order to judge it is operating safely?". Once the initial Runtime Verification (RV) concept is developed, researchers and practitioners have to deal with research and engineering challenges encountered during the realization of the RV approaches into an actual runtime monitoring solution for ADS. These challenges range from, estimating different safety parameters of the runtime monitors, finding solutions for different technical problems, to meeting scalability and efficiency requirements. The focus of this thesis is to propose novel runtime monitoring solutions for verifying the safe operation of ADS. This encompasses (i) defining novel RV approaches explicitly tailored for automated driving, and (ii) developing concepts, methods, and architectures for realizing the RV approaches into an actual runtime monitoring solution for ADS. Contributions to the former include defining two runtime RV approaches, namely the Computer Vision Monitor (CVM) and the Safe Driving Envelope Verification. Contributions to the latter include (i) estimating the sufficient diagnostic test interval of the runtime verification approaches (in particular the CVM), (ii) addressing the out-of-sequence measurement problem in sensor fusion-based ADS, and (iii) developing an architectural solution for improving the scalability and efficiency of the runtime monitoring solution. / RetNet

For people who are blind or have low vision (BLV), physical barriers and negative experiences related to using current transportation options can have negative impacts on quality of life. The emergence of levels 4 – 5 automated driving system-dedicated vehicles (L4+ ADS), which will not require human operators to provide any input into the dynamic driving task, could empower the BLV community by providing an independent means of transportation. Yet, the BLV community has concerns that their needs are not being adequately considered by those currently developing L4+ ADSs, which will result in this technology being inaccessible to populations that it would otherwise greatly benefit. The current study sought to address this gap in the literature by explicitly evaluating the information and interactions that BLV riders will require from L4+ ADS. Specifically, we collected focus group and empirical data across three studies on BLV riders' information and interaction requirements for L4+ ADSs across expected and unexpected driving scenarios as well as pick-up and drop-off tasks (PUDO). Through focus groups with sighted (n = 11) and BLV participants (n = 11; Study 1), we identified similarities and differences between sighted and BLV participants in terms of their user needs for L4+ ADSs across five challenging driving scenarios. Next, we examined BLV participants' (n = 13; Study 2) information requests in real-world settings to better understand BLV riders' needs during a simulated L4+ ADS experience. Our findings show that BLV riders want information that helps with (a) orienting to important objects in the environment during PUDO, (b) determining their location while riding in the ADS, and (c) understanding the ADSs' actions. Finally, we developed an HMI prototype using BLV riders' feedback in Studies 1 and 2 and had BLV participants engage with it during a simulated L4+ ADS trip (n = 12; Study 3). Our results suggest that BLV riders value information about nearby landmarks in familiar and unfamiliar areas, as well as explanations for ADS's actions during ordinary and unexpected scenarios. Additionally, BLV riders need information about required walking distances and presence of tripping hazards in order to select a drop-off location. Taken together, our studies show that BLV riders have specific requirements that L4+ ADS must meet in order for this to be an accessible means of transportation. In light of these findings, we generated 28 guidelines and 44 recommendations that could be used by designers to improve the accessibility of L4+ ADSs for BLV riders. / Doctor of Philosophy / When using current transportation options, individuals who are blind or have low vision (BLV) often encounter physical barriers and negative experiences, which can limit their ability to travel independently and have negative impacts on their overall quality of life. However, future vehicles equipped with levels 4 – 5 automated driving systems (L4+ ADSs) will offer transportation that requires no input from human operators, and thus, could be used as an independent means of transportation for the BLV community. Unfortunately, the BLV community has concerns that their needs are not being adequately considered by those currently developing L4+ ADSs, which will result in this technology being inaccessible to populations that it would otherwise greatly benefit. The current work sought to address this gap in the literature by evaluating the information and interactions that BLV riders will require from L4+ ADS. We conducted three studies to collected data on BLV riders' information and interaction requirements for L4+ ADSs across a variety of driving scenarios as well as tasks relating to being picked up and dropped-off by an L4+ ADS. First, through focus groups with sighted and BLV participants, we identified similarities and differences between sighted and BLV participants' user needs for L4+ ADSs across five challenging driving scenarios. Next, to better understand BLV riders' needs, we had BLV participants indicate when they would desire information during a simulated L4+ ADS ride-hailing experience in real-world settings. Our findings show that BLV riders want information that helps with (a) orienting to important objects in the environment during PUDO, (b) determining their location during their trip, and (c) understanding the reason for the ADS's actions. Finally, using BLV riders' feedback, we developed an HMI prototype and had BLV participants engage with it during a simulated L4+ ADS trip. Our results suggest that BLV riders value information about nearby landmarks in both familiar and unfamiliar areas, as well as explanations for ADS's actions during common (e.g., stopping at a stop sign) and unexpected driving scenarios (e.g., sudden swerve). Additionally, when being dropped off, BLV riders need information about required walking distances and presence of tripping hazards in order to select a desirable drop-off location. Taken together, our studies show that BLV riders have specific requirements that L4+ ADS must meet in order for this to be an accessible means of transportation. In light of these findings, we generated a set of guidelines and recommendations that designers can use to improve the accessibility of L4+ ADSs for BLV riders.

This study was performed with the goal of delineating how drivers' interactions with an Automated Driving System were affected by a Driver Monitoring System (DMS), which provided alerts to the driver when he or she became inattentive to the driving environment. There were two specific research questions. The first was centered on addressing how drivers' trust and satisfaction with an Automated Driving System was affected by a DMS. The second was centered on addressing how drivers' abilities to detect changes in the driving environment that required intervention were affected by the presence of a DMS. Data were collected from fifty-six drivers during a test-track experiment with an Automated Driving System prototype that was equipped with a DMS. DMS attention prompt conditions were treated as the independent variable and trust, satisfaction, and driver performance during the experimenter triggered lane drifts were treated as dependent variables. The findings of this investigation suggested that drivers who receive attention prompts from a DMS have lower levels of trust and satisfaction with the Automated Driving System compared to drivers who do not receive attention prompts from a DMS. While the DMS may result in lower levels of trust and satisfaction, the DMS may help drivers detect changes in the driving environment that require attention. Specifically, drivers who received attention prompts after 7 consecutive seconds of inattention were 5 times more likely to react to a lane drift with no alert compared to drivers who did not receive attention prompts at all. / Master of Science

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.

Driverless cars are a hot topic in today’s industry where several vehicle manufacturers try to create a reliable system for automated driving. The advantages of highly automated vehicles are many, safer roads and a lower environmental impact are some of the arguments for this technology. However, the notion of highly automated cars give rise to a large number of human factor issues regarding the safety and reliability of the automated system as well as concern about the driver’s role in the system. The purpose of this study was to explore the effects of systematic variations in traffic complexity and external time pressure on decision-making time in a simulated situation using a web-based testing platform. A secondary focus was to examine whether measures of cognitive performance and driver attitudes have an effect on decision-making time.  The results show that systematic variations in both time pressure and traffic complexity have an effect on decision-making time. This indicates that drivers are able to adapt their decision-making to facilitate the requirements of a certain situation. The results also indicate that intelligence; speed of processing and driver attitudes has an effect on decision-making time.

Das autonome Fahren ist derzeit aufgrund zahlreicher aktueller Forschungs- und Entwicklungsprojekte namhafter Automobilhersteller und -zulieferer im Fokus des öffentlichen Interesses. Der stetige Fortschritt des autonomen Fahrens kann unter anderem auf der jährlich in Las Vegas stattfindenden Consumer Electronics Show (CES) festgestellt werden, welche seit einiger Zeit auch von Automobilherstellern als Plattform zur Vorstellung neuer Technologien genutzt wird. So demonstrierte die Audi AG einen A7, der vollständig autonom vom Silicon Valley eine Strecke von 900 km Länge nach Las Vegas fuhr. Darüber hinaus legen auch automobilfremde Hochtechnologiekonzerne ihr Interesse an dieser Schlüsseltechnologie offen und präsentieren eigene Forschungs- und Entwicklungsarbeiten. Google verkündete die Forschung an einem eigenen autonomen Fahrzeug und auch Spekulationen über das Interesse von Apple wurden medial publik. Dennoch stehen die rasante Entwicklungsgeschwindigkeit und die öffentlich bereits präsentierte Funktionsfähigkeit des Autonomen Fahrens im Kontrast zu der geringen Anwendung im deutschen Straßenverkehr. In Deutschland und anderen Ländern sind bisher nur Pilotprojekte aus Forschung und Entwicklung existent. Diese unterliegen aktuell noch vielen Restriktionen. Dies macht deutlich, dass trotz der bisher erreichten Einzelerfolge dieser Technologie keine Serienreife besteht. So müssen für den tatsächlichen Einsatz des Autonomen Fahrens im Straßenverkehr technologische, soziologische sowie gesetzliche Rahmenbedingungen eingehalten, erweitert, angepasst, respektive erst noch definiert wer-den. Insbesondere im urbanen Umfeld besteht hier hoher Forschungsbedarf auch im Hin-blick auf technologische Rahmenbedingungen. Die vorliegende Arbeit soll einen Einblick über offene Fragestellungen und technologische Hürden sowie deren Bedeutung für das Autonome Fahren ermöglichen. Zudem wird ein kurzer Überblick darüber gegeben, wie dies auf den Sektor der Baumaschinen übertragbar ist.

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about driving environment. Studies have proved that automated vehicles have a potential to decrease traffic congestion on road networks by reducing the time headway, enhancing the traffic capacity and improving the safety margins in car following. Furthermore, vehicle movement and driver’s behavior of conventional vehicles will be affected by the presence of automated vehicles in traffic networks. Despite different encouraging factors, automated driving raises some concerns such as possible loss of situation awareness, overreliance on automation and degrading driving skills in absence of practice. Moreover, coping with complex scenarios, such as merging at ramps and overtaking, in terms of interaction between automated vehicles and conventional vehicles need more research. This thesis work aims to investigate the effects of automated vehicles on driver’s behavior and traffic performance. A broad literature review in the area of driving simulators and psychological studies was performed to examine the automated vehicle effects on driver’s behavior. Findings from the literature survey, which has been served as setup values in the simulation study of the current work, reveal that the conventional vehicles, which are driving close to the platoon of automated vehicles with short time headway, tend to reduce their time headway and spend more time under their critical time headway. Additionally, driving highly automated vehicles is tedious in a long run, reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of automated vehicles on traffic performance, a microscopic simulation case study consisting of different penetration rates of automated vehicles (0, 50 and 100 percentages) was conducted in VISSIM software. The scenario network is a three-lane autobahn segment of 2.9 kilometers including an off-ramp, on-ramp and a roundabout with some surrounding urban roads. Outputs of the microscopic simulation in this study reveal that the positive effects of automated vehicles on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably decreased by 8.09% during p.m. peak hours in scenario with automated vehicles. Besides, Smoother traffic flow with less queue in the weaving segment was observed. Result of the scenario with 50% share of automated vehicles moreover shows a feasible interaction between conventional vehicles and automated vehicles. Meaningful outputs of this case study, based on the input data from literature review, demonstrate the capability of VISSIM software to simulate the presence of automated vehicles in great extent, not only as an automated vehicle scenario but also a share of them, in traffic network. The validity of the output values nonetheless needs future research work on urban and rural roads with different traffic conditions.

Over the last years, driving automation has increasingly moved into focus in human factors research. A large body of research focusses on situations in which the human driver needs to regain control. However, little research has so far been conducted on how SAE level 3+ automated driving should be designed with focus on occupant comfort. This thesis aims at identifying a comfortable driving style for automated vehicles. As a basis, it was necessary to pinpoint driving metrics, which vary between driving styles and can be manipulated in order to design a comfortable driving style. Hence, Study 1 was conducted, in which drivers (N = 24) manually drove on a highway or on urban and rural roads with certain driving styles. Results show relevant metrics (i.e., lateral and longitudinal acceleration, lateral and longitudinal jerk, quickness, and headway distance in seconds) and that these metrics vary across maneuvers and thus, a maneuver-specific analysis is recommended. As these metrics are derived from manual data, it remained unclear after Study 1, in which range the metric values should vary for comfortable automated driving. Therefore, as a second step, the main metrics were varied and the subsequent combinations implemented in an automated vehicle as well as in a dynamic simulator with two different configurations. The combinations were then subject to ratings by 72 participants. Results show that the metrics and values found in Study 1, are able to elicit a range of comfort ratings in automated driving. It was also found, that acceleration is a key variable in experiencing comfort. However, it is not the sole predictor. Additionally, as higher levels of automated driving with larger velocities are still bound to considerable constraints for on-road testing, the second study was also used to validate a dynamic driving simulator to allow comfort during automated driving to be studied. In comparison to ratings on a test track, the dynamic simulator setting with longitudinal orientation is able to show both relative and absolute validity of comfort ratings. In the third and final step, different approaches to automated maneuvers were rated by participants (N = 72) regarding the comfort they experienced. A lane change, an acceleration, and a deceleration maneuver were chosen as test maneuvers. The lateral or longitudinal acceleration was varied in each of these maneuvers. Results, again, show comfort ratings are maneuver specific. On one hand, symmetrical and early-onset lane change maneuvers and symmetrical acceleration maneuvers were preferred. However, symmetrical deceleration maneuvers and deceleration maneuvers with a slower acceleration decrease evoke the highest comfort ratings. These ratings made it possible to offer guidelines for the design of automated driving styles. Furthermore, dependence on a number of personality traits was analyzed. Results suggest the general preference for certain driving styles to be unaffected by personality. However, it seems, participants with certain personality types are less particular about their preference for certain driving styles. Summed up, comfortable automated driving is – under the investigated circumstances – characterized by maneuvers with sufficient headway distance and smooth applications of small acceleration and small jerk. These should, even so, still provide sufficient motion feedback. Surrounding traffic seems to play an important role through urgency and should be considered for on-road implementation. Differences in personality did not seem to play a crucial role.