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.
@article{arxiv.2412.16248,
title = {Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed},
author = {Benny Bao-Sheng Li and Elena Wu and Hins Shao-Xuan Yang and Nicky Yao-Jin Liang},
journal= {arXiv preprint arXiv:2412.16248},
year = {2024}
}