用行为克隆与近端策略优化实现路径跟踪与静态障碍物避让
Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
- 先用行为克隆模仿专家路径跟踪，再用PPO调整路径避开固定障碍
- 实验表明可精准跟踪路径且避免碰撞，性能接近专家水平
- 适合需要学习型规划的自动驾驶场景，兼具可解释性与灵活性
在自动驾驶中，基于模仿学习（IL）和强化学习（RL）的端到端方法日益流行。然而，这些方法缺乏经典机器人学中的显式推理与前瞻规划，导致策略隐式且短视。本文提出一种路径规划方法：使用行为克隆（BC）进行路径跟踪，利用近端策略优化（PPO）实现静态障碍物避让。该方法输出横向偏移值以修正参考航点，生成适配不同控制器的优化路径。实验结果表明，该算法能有效模仿专家路径跟踪控制器的表现，并成功避开固定障碍物。该方法为自动驾驶路径规划问题中基于学习的方法提供了有益尝试。
原文摘要 · Abstract (English)
In autonomous driving, end-to-end methods utilizing Imitation Learning (IL) and Reinforcement Learning (RL) are becoming more and more common. However, they do not involve explicit reasoning like classic robotics workflow and planning with horizons, resulting in strategies implicit and myopic. In this paper, we introduce a path planning method that uses Behavioral Cloning (BC) for path-tracking and Proximal Policy Optimization (PPO) for static obstacle nudging. It outputs lateral offset values to adjust the given reference waypoints and performs modified path for different controllers. Experimental results show that the algorithm can do path following that mimics the expert performance of path-tracking controllers, and avoid collision to fixed obstacles. The method makes a good attempt at planning with learning-based methods in path planning problems of autonomous driving.
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