  Verification and validation are major challenges for developing automated
driving systems. A concept that gets more and more recognized for testing in
automated driving is scenario-based testing. However, it introduces the problem
of what scenarios are relevant for testing and which are not. This work aims to
find relevant, interesting, or critical parameter sets within logical scenarios
by utilizing Bayes optimization and Gaussian processes. The parameter
optimization is done by comparing and evaluating six different metrics in two
urban intersection scenarios. Finally, a list of ideas this work leads to and
should be investigated further is presented.
