Generating critical test scenarios for automated vehicles with evolutionary algorithms

Virtual testing of automated vehicles using simulations is essential during their development. When it comes to the testing of motion planning algorithms, one is mainly interested in challenging, critical scenarios for which it is hard to find a feasible solution. However, these situations are rare under usual traffic conditions, demanding an automatic generation of critical test scenarios. We present an approach that automatically generates critical scenarios based on a minimization of the solution space of the vehicle under test. By formulating a scenario parametrization and automatic determination of relevant parameter intervals, we are able to optimize the criticality of complex scenarios. We use evolutionary algorithms to tackle the resulting highly nonlinear optimization problem. Compared to our previous approach, we are now able to handle complex situations, in particular those involving intersections. Finally, we demonstrate our approach by generating critical scenarios from initially uncritical scenarios.