強化学習に基づく複雑な運転シナリオ向けの安全な階層型計画フレームワーク
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
低レベルの安全な制御器群と高レベルの強化学習コーディネータを組み合わせ、安全性を保証しつつ複雑な交通状況で効率的に意思決定する階層型行動計画を提案した。
著者: Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based motion planner cannot efficiently generate safe trajectories in real time, particularly when there are many interactive vehicles near by. On the other hand, end-to-end learning methods cannot assure the safety of the outcomes. To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers. Safety is guaranteed by the low-level optimization/sampling-based controllers, while the high-level reinforcement learning algorithm makes H-CtRL an adaptive and efficient behavior planner. To train and test our proposed algorithm, we built a simulator that can reproduce traffic scenes using real-world datasets. The proposed H-CtRL is proved to be effective in various realistic simulation scenarios, with satisfying performance in terms of both safety and efficiency.
