悪天候下の自動運転知覚はどう失敗するか：強化学習によるストレステスト
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
強化学習を用いて、雨・霧・雪などの悪天候でLiDAR知覚や軌道予測が失敗しやすい入力の乱れ系列を探索する手法を提案し、実走行データで有効性を示した。
著者: Harrison Delecki, Masha Itkina, Bernard Lange, Ransalu Senanayake, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse weather such as rain, fog, and snow. However, validation of these systems is challenging due to their complexity and dependence on observation histories. This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions. We develop a methodology based in reinforcement learning to find likely failures in object tracking and trajectory prediction due to sequences of disturbances. We apply disturbances using a physics-based data augmentation technique for simulating LiDAR point clouds in adverse weather conditions. Experiments performed across a wide range of driving scenarios from a real-world driving dataset show that our proposed approach finds high likelihood failures with smaller input disturbances compared to baselines while remaining computationally tractable. Identified failures can inform future development of robust perception systems for AVs.
