安全性を強化した深層強化学習による自動運転
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
ルールベースと学習ベースの安全機構を組み合わせ、将来の衝突リスクを予測して報酬に反映する自動運転向け強化学習システムを提案した。
著者: Ali Baheri, Subramanya Nageshrao, H. Eric Tseng, Ilya Kolmanovsky, Anouck Girard, Dimitar Filev
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches for safety assurance. Our safety system consists of two modules namely handcrafted safety and dynamically-learned safety. The handcrafted safety module is a heuristic safety rule based on common driving practice that ensure a minimum relative gap to a traffic vehicle. On the other hand, the dynamically-learned safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, the dynamically-leaned safety module incorporates a model lookahead beyond the immediate reward of reinforcement learning to predict safety longer into the future. If one of the future states leads to a near-miss or collision, then a negative reward will be assigned to the reward function to avoid collision and accelerate the learning process. We demonstrate the capability of the proposed framework in a simulation environment with varying traffic density. Our results show the superior capabilities of the policy enhanced with dynamically-learned safety module.
