低速自動運転の最適化：経路安定性と最高速度のための強化学習アプローチ
Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
強化学習を用いて、低速自動運転において経路追従を保ちながら安全に最高速度を維持する運転ポリシーを最適化した研究。
著者: Benny Bao-Sheng Li, Elena Wu, Hins Shao-Xuan Yang, Nicky Yao-Jin Liang
分類: cs.AI, cs.RO
原文アブストラクト
Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios.