用强化学习动态调优纯追踪参数，提升赛车自主导航性能。
Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO
- 用PPO算法在线联合优化前瞻距离和转向增益
- 在仿真与实车测试中均优于固定参数与传统自适应方法
- 无需针对赛道重调参，适合实际自动驾驶赛车应用
纯追踪（Pure Pursuit, PP）因高效且几何清晰，被广泛用于自主赛车的实时路径跟踪，但其性能高度依赖于前瞻距离和转向增益等关键参数的设置。传统的基于速度的参数调度仅近似调整，且难以跨赛道和速度配置迁移。本文提出一种基于强化学习的方法，使用近端策略优化（PPO）联合在线选择前瞻距离（Ld）和转向增益（g）。策略通过观测紧凑的状态特征（速度与曲率采样点）输出每步的（Ld, g）；在F1TENTH Gym中训练，并部署于ROS 2系统。该策略直接驱动PP（仅需轻度平滑），无需针对特定地图重新调参。在仿真与实车测试中，所提出的RL-PP控制器持续优于固定前瞻距离的PP、基于速度的自适应PP及仅优化前瞻距离的强化学习变体，并在圈速、路径跟踪精度和转向平滑性上超越了所评估条件下的运动学模型预测控制（kinematic MPC）赛车线跟踪器，证明了策略引导的参数调优可稳定提升经典几何控制的性能。
原文摘要 · Abstract (English)
Pure Pursuit (PP) is widely used in autonomous racing for real-time path tracking due to its efficiency and geometric clarity, yet performance is highly sensitive to how key parameters-lookahead distance and steering gain-are chosen. Standard velocity-based schedules adjust these only approximately and often fail to transfer across tracks and speed profiles. We propose a reinforcement-learning (RL) approach that jointly chooses the lookahead Ld and a steering gain g online using Proximal Policy Optimization (PPO). The policy observes compact state features (speed and curvature taps) and outputs (Ld, g) at each control step. Trained in F1TENTH Gym and deployed in a ROS 2 stack, the policy drives PP directly (with light smoothing) and requires no per-map retuning. Across simulation and real-car tests, the proposed RL-PP controller that jointly selects (Ld, g) consistently outperforms fixed-lookahead PP, velocity-scheduled adaptive PP, and an RL lookahead-only variant, and it also exceeds a kinematic MPC raceline tracker under our evaluated settings in lap time, path-tracking accuracy, and steering smoothness, demonstrating that policy-guided parameter tuning can reliably improve classical geometry-based control.
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