自動運転車のための適応的ストレステスト
Adaptive Stress Testing for Autonomous Vehicles
自動運転車の意思決定システムを検証するため、環境の確率的要素を摂動させて衝突シナリオを探索する手法を提案し、MCTSと深層強化学習で最も起こりやすい失敗事例を効率的に見つけられることを示した。
著者: Mark Koren, Saud Alsaif, Ritchie Lee, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.LG, stat.ML
原文アブストラクト
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.
