多様な交通行動シミュレーションと反事実分析による自動運転プランナの回避可能な失敗の発見
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
行動的多様性を持つ交通参加者のシミュレーション上で大規模探索を行い、衝突に至る動的シナリオを生成・検出し、不可避事故と回避可能事故を区別してプランナ固有の欠陥を自動的に見つける枠組みを提案した。
著者: Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama, Shinya Shiroshita, Karim Hamzaoui, Yi Ouyang, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
分類: cs.LG, cs.RO
原文アブストラクト
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the core decision-making component of autonomous robots: their planning algorithm. We introduce a planner testing framework that leverages recent progress in simulating behaviorally diverse traffic participants. Using large scale search, we generate, detect, and characterize dynamic scenarios leading to collisions. In particular, we propose methods to distinguish between unavoidable and avoidable accidents, focusing especially on automatically finding planner-specific defects that must be corrected before deployment. Through experiments in complex multi-agent intersection scenarios, we show that our method can indeed find a wide range of critical planner failures.
