Autonomes Fahren (AF) wird potenziell die Präferenzen für die im Auto verbrachte Zeit stark beeinflussen und dementsprechend den Wert der Reisezeit, der ein Schlüsselelement von Kosten-Nutzen-Analysen im Verkehr ist. Die Untersuchung dieses Aspekts des AF ist daher entscheidend für die Analyse potenzieller Auswirkungen der Technik auf die zukünftige Verkehrsnachfrage. Trotz der steigenden Anzahl an Studien zu diesem Thema, gibt es noch erhebliche Forschungslücken. Der Fokus der Dissertation ist die potenziellen Änderungen des Reisezeitwerts, die durch das AF entstehen, zu messen sowie ihre Determinanten zu analysieren. Es wurden sowohl qualitative Ansätze als auch quantitative Methoden verwendet. Dabei wurden zwei Konzepte von AF betrachtet: privates und geteiltes autnomes Fahrzeug. Die Ergebnisse der Analysen zeigen einen niedrigeren Wert der Reisezeitersparnis beim AF im Vergleich zum manuellen Fahren, allerdings nur auf Pendelwegen. Das private Fahrzeug wird als eine attraktivere Option als ein geteiltes Fahrzeug wahrgenommen, jedoch unterscheiden sich die Nutzerpräferenzen für geteilte Fahrzeug stark zwischen den durchgeführten Studien. Individuelle Charakteristiken, wie Erfahrung mit Fahrassistenzsystemen, beeinflussen stark die Wahrnehmung der Zeit im AF; andere sozio-demographischen Faktoren, wie Alter und Geschlecht haben vor allem einen indirekten Effekt auf den Reisezeitwert indem sie Einstellungen potenzieller Nutzer beeinflussen. Die Verbesserung des Fahrterlebnisses durch das AF und das Vertrauen in die Technik sind wichtige Determinanten der Reisezeitwahrnehmung. Fahrvergnügen und andere wahrgenommene Vorteile vom manuellen Fahren gleichen in einem gewissen Ausmaß den Nutzen vom AF aus. Es wurden Reisezeitwerte für unterschiedliche potenzielle Nutzersegmente berechnet. Abschließend wurden politische Implikationen, Empfehlungen für die Entwicklung von AF sowie Empfehlungen für künftige Studien und potenziellen Forschungsgebiete abgeleitet. / Autonomous driving will potentially strongly affect preferences for time spent in a vehicle and, consequently, the value of travel time savings (VTTS). As VTTS is a key element of cost-benefit analysis for transport, these interrelations are crucial for analysing the potential impact of the technology on future travel demand. Despite the increasing number of studies dedicated to this topic there are still many unanswered questions. The focus of the thesis is to measure potential changes in the VTTS resulting from the introduction of autonomous driving and analyse their determinants. Qualitative approaches and quantitative methods were used. Two concepts of AVs were considered: a privately-owned AV (PAV) and a shared AV (SAV). The analysis results suggest lower VTTS for autonomous driving compared to manual driving, but only on commuting trips. A PAV is perceived as a more attractive option than an SAV, but user preferences for SAVs vary between the conducted studies. Individual characteristics, such as experience with advanced driver assistance systems, strongly affect the perception of time in an AV; other socio-demographic factors, such as age and gender, affect mode choices and the VTTS mainly indirectly by influencing the attitudes of potential users. The improvement in travel experiences due to autonomous driving and trust in the technology are important determinants of the perception of travel time. Enjoyment of driving and other perceived benefits of manual driving partially counterbalance the utility of riding autonomously. VTTS for different potential user segments were calculated. In conclusion, several policy implications, development recommendations for AVs as well as recommendations for future studies and potential research avenues are derived from the findings.

New automated vehicles have the chance of high improvements to road safety. Nevertheless, from today's perspective, accidents will always be a part of future mobility. Following the “Vision Zero”, this thesis proposes the quantification of the driving situation's criticality as the basis to intervene by newly integrated safety systems. In the example application of trajectory planning, a continuous, real-time, risk-based criticality measure is used to consider uncertainties by collision probabilities as well as technical accident severities. As result, a smooth transition between preventative driving, collision avoidance, and collision mitigation including impact point localization is enabled and shown in fleet data analyses, simulations, and real test drives. The feasibility in automated driving is shown with currently available test equipment on the testing ground. Systematic analyses show an improvement of 20-30 % technical accident severity with respect to the underlying scenarios. That means up to one-third less injury probability for the vehicle occupants. In conclusion, predicting the risk preventively has a high chance to increase the road safety and thus to take the “Vision Zero” one step further.:Abstract Acknowledgements Contents Nomenclature 1.1 Background 1.2 Problem statement and research question 1.3 Contribution 2 Fundamentals and relatedWork 2.1 Integral safety 2.1.1 Integral applications 2.1.2 Accident Severity 2.1.2.1 Severity measures 2.1.2.2 Severity data bases 2.1.2.3 Severity estimation 2.1.3 Risk assessment in the driving process 2.1.3.1 Uncertainty consideration 2.1.3.2 Risk as a measure 2.1.3.3 Criticality measures in automated driving functions 2.2 Operational motion planning 2.2.1 Performance of a driving function 2.2.1.1 Terms related to scenarios 2.2.1.2 Evaluation and approval of an automated driving function 2.2.2 Driving function architecture 2.2.2.1 Architecture 2.2.2.2 Planner 2.2.2.3 Reference planner 2.2.3 Ethical issues 3 Risk assessment 3.1 Environment model 3.2 Risk as expected value 3.3 Collision probability and most probable collision configuration 4 Accident severity prediction 4.1 Mathematical preliminaries 4.1.1 Methodical approach 4.1.2 Output definition for pedestrian collisions 4.1.3 Output definition for vehicle collisions 4.2 Prediction models 4.2.1 Eccentric impact model 4.2.2 Centric impact model 4.2.3 Multi-body system 4.2.4 Feedforward neural network 4.2.5 Random forest regression 4.3 Parameterisation 4.3.1 Reference database 4.3.2 Training strategy 4.3.3 Model evaluation 5 Risk based motion planning 5.1 Ego vehicle dynamic 5.2 Reward function 5.3 Tuning of the driving function 5.3.1 Tuning strategy 5.3.2 Tuning scenarios 5.3.3 Tuning results 6 Evaluation of the risk based driving function 6.1 Evaluation strategy 6.2 Evaluation scenarios 6.3 Test setup and simulation environment 6.4 Subsequent risk assessment of fleet data 6.4.1 GIDAS accident database 6.4.2 Fleet data Hamburg 6.5 Uncertainty-adaptive driving 6.6 Mitigation application 6.6.1 Real test drives on proving ground 6.6.2 Driving performance in simulation 7 Conclusion and Prospects References List of Tables List of Figures A Extension to the tuning process