Learning to falsify automated driving vehicles with prior knowledge

• URL: http://arxiv.org/abs/2101.10377v1
• Date: Mon, 25 Jan 2021 19:51:38 GMT
• Title: Learning to falsify automated driving vehicles with prior knowledge
• Authors: Andrea Favrin and Vladislav Nenchev and Angelo Cenedese
• Abstract summary: 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 in a model-based falsifier to guide and improve the learning process.
• Score: 1.4610038284393165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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