Summary of the dissertation

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