Summary of the Dissertation

To ensure the safe operation of automated vehicles in urban environments, driving functions must be thoroughly tested. The wide variety of possible traffic situations poses a challenge in this regard. This dissertation focuses on unsupervised machine learning methods to extract situations relevant to testing from traffic datasets. This enables automated driving functions to be tested realistically in simulations and helps make urban traffic safer in the future.