安全で快適な自動運転のための深層強化学習とプランニングのハイブリッド手法
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving
歩行者予測とリスク考慮型プランニングを深層強化学習に組み込み、安全で快適な自動運転ポリシーを効率的に学習するHyLEARを提案し、CARLAベンチマークで有効性を示した。
著者: Dikshant Gupta, Mathias Klusch
分類: cs.RO, cs.AI
原文アブストラクト
We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.