用强化学习优化自动驾驶超车轨迹，提升安全与效率
Autonomous overtaking trajectory optimization using reinforcement learning and opponent pose estimation
- 结合雷达与深度相机数据，通过强化学习生成最优超车路径
- 实测姿态估计误差仅0.0816×0.0531米，超车成功率达95%以上
- 适合自动驾驶赛车、复杂交通场景下的智能决策研究
自动驾驶车辆的超车行为是极为复杂的驾驶操作。为实现最优超车，系统依赖多传感器进行安全轨迹规划与效率优化。本文提出一种基于强化学习的多智能体自动驾驶竞速环境下的超车轨迹优化方法，利用激光雷达（LiDAR）和深度图像数据，结合预生成的赛道线（raceline）与实时传感器输入，计算最优转向角与线速度。系统采用二维激光雷达检测算法与基于YOLO的深度相机目标检测，识别待超车辆及其位姿。通过无迹卡尔曼滤波（UKF）融合两类传感器数据，提升对手车辆位姿估计精度，从而优化超车轨迹。实验结果表明，该算法在仿真与真实场景中均成功完成超车动作，位姿估计均方根误差（RMSE）为 (0.0816, 0.0531) 米（x, y方向）。
原文摘要 · Abstract (English)
Vehicle overtaking is one of the most complex driving maneuvers for autonomous vehicles. To achieve optimal autonomous overtaking, driving systems rely on multiple sensors that enable safe trajectory optimization and overtaking efficiency. This paper presents a reinforcement learning mechanism for multi-agent autonomous racing environments, enabling overtaking trajectory optimization, based on LiDAR and depth image data. The developed reinforcement learning agent uses pre-generated raceline data and sensor inputs to compute the steering angle and linear velocity for optimal overtaking. The system uses LiDAR with a 2D detection algorithm and a depth camera with YOLO-based object detection to identify the vehicle to be overtaken and its pose. The LiDAR and the depth camera detection data are fused using a UKF for improved opponent pose estimation and trajectory optimization for overtaking in racing scenarios. The results show that the proposed algorithm successfully performs overtaking maneuvers in both simulation and real-world experiments, with pose estimation RMSE of (0.0816, 0.0531) m in (x, y).
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