Summary of the Dissertation

Future mobility systems must operate safely and efficiently in increasingly complex urban environments. To ensure this, thorough testing of assisted and automated driving systems is crucial, particularly through scenario-based approaches that cover a wide range of traffic situations. This work examines critical scenarios using Bayesian optimization to efficiently identify relevant scenarios and summarize the results into a reduced catalog that serves as the basis for comprehensive testing.