事前知識を活用した自動運転車の反例探索学習
Learning to falsify automated driving vehicles with prior knowledge
自動運転機能の仕様違反シナリオを効率的に見つけるため、事前知識で探索を制限・誘導する学習ベースの反例探索フレームワークを提案し、適応クルーズコントロールで有効性を示した。
著者: Andrea Favrin, Vladislav Nenchev, Angelo Cenedese
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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.
