確率的制御バリア関数を用いた安全な強化学習によるランプ合流
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging
モデル不確かさを確率的制御バリア関数で推定し、強化学習に組み込むことで、高速道路のランプ合流における安全かつ効率的な自動運転ポリシーを実現した。
著者: Soumith Udatha, Yiwei Lyu, John Dolan
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control barrier functions embedded into the reinforcement learning policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle. However, control barrier functions need a good approximation of the model of the car. We use probabilistic control barrier functions as an estimate of the model uncertainty. The algorithm is implemented as an online version in the CARLA (Dosovitskiy et al., 2017) Simulator and as an offline version on a dataset extracted from the NGSIM Database. The proposed algorithm is not just a safe ramp merging algorithm but a safe autonomous driving algorithm applied to address ramp merging on highways.
関連論文
- 人間のリスク選好に整合した安全強化学習による密集交通での高速道路合流安全強化学習/自動運転
