DynNPC: 動的NPC行動生成による自動運転システムのシミュレーションテストでの違反誘発シナリオ探索
DynNPC: Finding More Violations Induced by ADS in Simulation Testing through Dynamic NPC Behavior Generation
自動運転システムのテストにおいて、NPC車両が交通信号や自車の挙動に応じて動的に行動を生成するフレームワークを提案し、より現実的で違反を誘発するシナリオを効率的に見つける。
著者: You Lu, Yifan Tian, Dingji Wang, Bihuan Chen, Xin Peng
分類: cs.SE, cs.RO
原文アブストラクト
Recently, a number of simulation testing approaches have been proposed to generate diverse driving scenarios for autonomous driving systems (ADSs) testing. However, the behaviors of NPC vehicles in these scenarios generated by previous approaches are predefined and mutated before simulation execution, ignoring traffic signals and the behaviors of the Ego vehicle. Thus, a large number of the violations they found are induced by unrealistic behaviors of NPC vehicles, revealing no bugs of ADSs. Besides, the vast scenario search space of NPC behaviors during the iterative mutations limits the efficiency of previous approaches. To address these limitations, we propose a novel scenario-based testing framework, DynNPC, to generate more violation scenarios induced by the ADS. Specifically, DynNPC allows NPC vehicles to dynamically generate behaviors using different driving strategies during simulation execution based on traffic signals and the real-time behavior of the Ego vehicle. We compare DynNPC with state-of-the-art scenario-based testing approaches. Our evaluation has demonstrated the effectiveness and efficiency of DynNPC in finding more violation scenarios induced by the ADS.
