Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed

Autor:	Li, Benny Bao-Sheng, Wu, Elena, Yang, Hins Shao-Xuan, Liang, Nicky Yao-Jin
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Robotics
Zdroj:	Journal of Autonomous Systems, Volume 12, Issue 1, Pages 1-11, December 2024
Druh dokumentu:	Working Paper
DOI:	10.48550/arXiv.2412.16248
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.16248 Zobrazit plný text záznamu View this record from Arxiv