Summary of the dissertation

For the safe use of automated vehicles in an urban environment, the driving functions must be comprehensively tested. The variety of possible traffic situations is a challenge here. The dissertation deals with methods of unsupervised machine learning in order to extract the situations relevant for the test from traffic data sets. In this way, automated driving functions can be realistically tested in the simulation and the urban traffic of the future can be made safer.