Learning to falsify automated driving vehicles with prior knowledge

While automated driving technology has achieved a tremendous progress, the scalable and rigorous testing and verification of safe automated and autonomous driving vehicles remain challenging. Assuming that the specification is associated with a violation metric on possible scenarios, this paper proposes a learning-based falsification framework for testing the implementation of an automated or self-driving function in simulation. Prior knowledge is incorporated to limit the scenario parameter variance and into a model-based falsifier to guide and improve the learning process. For an exemplary adaptive cruise controller, the presented framework yields non-trivial falsifying scenarios with higher reward, compared to scenarios obtained by purely learning-based or purely model-based falsification approaches.