用强化学习提升低速自动驾驶的稳定性和最高速度
Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
- 基于强化学习优化驾驶策略，实现路径跟随与速度控制的平衡
- 在低速场景下达成接近理论最大速度的稳定行驶
- 适合关注自动驾驶控制稳定性与性能优化的研究者
近年来，自动驾驶受到广泛关注，尤其在复杂条件下优化车辆性能方面。本文针对低速自动驾驶中保持最大速度稳定性的问题，提出一种基于强化学习（RL）的新方法。该方法优化驾驶策略，使车辆在遵循预设路线的同时，实现接近最大速度的稳定行驶，且不牺牲安全性或路径准确性，即使在低速场景下也表现良好。
原文摘要 · Abstract (English)
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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