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In recent years, the automotive sector has seen a steady increase in the introduction of new Advanced Driving Assistance Systems (ADAS). This trend toward more complex systems will become even more pronounced with regard to Highly Automated Driving (HAD). In addition to the expected benefits of ADAS and HAD (increased comfort, efficiency, and safety), it is important to eliminate risks as much as possible to ensure that the system does not introduce new critical situations or road traffic accidents. Due to the increasing interaction of systems with the driver and their environment, it is no longer sufficient to investigate the system in isolation. There is also a need to investigate how the driver and the environment interact with the new system. Furthermore, the functional scope of the systems is expanding to cover entire application domains, such as highways and in the future rural and urban areas. This results in a significant increase in the number of parameters and scenarios that require testing for approval of these new technologies. This means that the scenario space to be analyzed is constantly expanding, which poses increasing problems for safety assessments. The expected number of test kilometers required to validate HAD is too large to be cost- and time-effective through real-world testing. This is why virtual safety assessments are necessary. In this context, the present thesis investigates whether virtual safety assessments can be efficiently performed today through Monte Carlo simulations using cognitive driver behavior models. The body of the thesis consists of four articles that consider different aspects of the safety assessment. Article 1 derives the cognitive core functions that driver behavior models must implement to display the causes and mechanisms of human error. This way, driver behavior models are able to map all hazard levels of realistic traffic, including normal traffic, critical situations, and road traffic accidents. By mapping the interactions of road users, cognitive models thus form the basis for the virtual safety assessment of ADAS and HAD systems. Due to the lack of existing cognitive driver behavior models that implement these cognitive core functions, the Driver Reaction Model (DReaM), a new driver behavior model, was developed and continuously improved as part of this work. Article 2 outlines a calibration and validation strategy, using DReaM as an example, to investigate whether driver behavior models are suitable for safety assessments, mapping all levels of realistic traffic. Subsequently, Article 3 estimates the time required to perform Monte Carlo studies for safety assessments, again using DReaM as an example. Therefore, an optimistic and pessimistic estimation is generated based on the minimum number of runs (MNR) required to simulate an exemplary traffic scenario. In summary, Articles 1–3 examine the quality of driver behavior models and the time required to perform safety-related studies. This lays the foundation for determining whether efficient safety assessments are feasible. Finally, Article 4 exemplarily assesses an urban Automatic Emergency Braking (AEB) system using DReaM to outline the overall virtual assessment methodology. Based on Article 4 and the findings of Article 1–3, minimal requirements are defined for improving and standardizing the virtual safety assessment process. These requirements aim to improve the reliability of safety assessments and enhance the comparability of results across various studies and models |
