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arXiv · 2412.16248

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

Benny Bao-Sheng Li·Elena Wu·Hins Shao-Xuan Yang·Nicky Yao-Jin Liang
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Abstract

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.

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Benny Bao-Sheng Li, Elena Wu, Hins Shao-Xuan Yang, Nicky Yao-Jin Liang. 2024-12-20. Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed. https://doi.org/10.48550/arxiv.2412.16248

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