  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. This paper proposes a learning-based
falsification framework for testing the implementation of an automated or
self-driving function in simulation. We assume that the function specification
is associated with a violation metric on possible scenarios. Prior knowledge is
incorporated to limit the scenario parameter variance and in 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.

Robotics;Artificial Intelligence;Systems and Control;Systems and Control