The safety validation of motion planning algorithms for automated vehicles requires a large amount of data for virtual testing. Currently, this data is often collected through real test drives, which is expensive and inefficient, given that only a minority of traffic scenarios pose challenges to motion planners. We present a workflow for generating a database of challenging and safety-critical test scenarios that is not dependent on recorded data. First, we extract a large variety of road networks across the globe from OpenStreetMap. Subsequently, we generate traffic scenarios for these road networks using the traffic simulator SUMO. In the last step, we increase the criticality of these scenarios using nonlinear optimization. Our generated scenarios are publicly available on the CommonRoad website.     «

The safety validation of motion planning algorithms for automated vehicles requires a large amount of data for virtual testing. Currently, this data is often collected through real test drives, which is expensive and inefficient, given that only a minority of traffic scenarios pose challenges to motion planners. We present a workflow for generating a database of challenging and safety-critical test scenarios that is not dependent on recorded data. First, we extract a large variety of road networ...     »