用Frenet坐标系提升自动驾驶轨迹跟踪精度
Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient
- 将车辆状态转至Frenet坐标系，更精准描述偏离道路中心的程度
- 基于DDPG的控制方法在复杂环境中实现高精度稳定追踪
- 适合自动驾驶与智能交通系统中的路径规划应用
本文研究深度确定性策略梯度（DDPG）算法在轨迹跟踪任务中的应用，提出一种结合弗雷内（Frenet）坐标系的轨迹跟踪控制方法。通过将车辆的位置和速度信息从笛卡尔坐标系转换到弗雷内坐标系，该方法能更准确地描述车辆相对于道路中心线的偏移量和行驶距离。DDPG算法采用演员-评论家框架，利用深度神经网络进行策略与价值评估，并结合经验回放机制和目标网络，提升了算法的稳定性与数据利用效率。实验结果表明，在复杂环境下，基于弗雷内坐标系的DDPG算法在轨迹跟踪任务中表现优异，实现了高精度且稳定的路径追踪，展现出在自动驾驶与智能交通系统中的应用潜力。
原文摘要 · Abstract (English)
This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle's position and velocity information from the Cartesian coordinate system to Frenet coordinate system, this method can more accurately describe the vehicle's deviation and travel distance relative to the center line of the road. The DDPG algorithm adopts the Actor-Critic framework, uses deep neural networks for strategy and value evaluation, and combines the experience replay mechanism and target network to improve the algorithm's stability and data utilization efficiency. Experimental results show that the DDPG algorithm based on Frenet coordinate system performs well in trajectory-tracking tasks in complex environments, achieves high-precision and stable path tracking, and demonstrates its application potential in autonomous driving and intelligent transportation systems. Keywords- DDPG; path tracking; robot navigation
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