Critical and Challenging Scenario Generation based on Automatic Action Behavior Sequence Optimization: 2021 IEEE Autonomous Driving AI Test Challenge Group 108

Assuring the safety of automated and autonomous driving functions is crucial for a safe deployment of self-driving vehicles on public roads. This includes the need for automated virtual testing methods and exhaustive search for critical scenarios that potentially reveal faults in the driving feature under test. In the past, researches have demonstrated the effectiveness of search-based testing to create situations that result in unintended behavior of the driving feature. In this paper, we contribute to this field of research by developing a method for automated generation of diverse critical scenarios based on a search algorithm that iterative optimizes behavior action sequences of the surrounding traffic participants towards critical situations. Utilizing the provided LG SVL Simulator pipeline, our method effectively generated both critical and challenging test scenarios that either revealed faulty behavior of the ego-vehicle (crash or near-crash) or showed extraordinary behavior of the surrounding traffic participants (e.g. approaching traffic on wrong lane).