用强化学习动态调整赛车路径追踪的前瞻距离，提升弯道与直道表现。
Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing
- 用PPO算法根据车速和曲率实时调整前瞻距离
- 在蒙特利尔和亚斯马林赛道实现出色的圈速成绩
- 可零样本迁移至真实赛车平台，适合自主驾驶研发
Pure Pursuit（PP）因其简洁性和实时性被广泛用于自动驾驶车辆路径跟踪。然而其性能对前瞻距离选择敏感：较短值提升弯道表现但导致直道不稳，较长值增强平滑性却降低曲线精度。本文提出一种混合控制框架，将近端策略优化（PPO）与经典纯追逐控制器结合，实现赛车过程中前瞻距离的动态调整。PPO代理根据车辆速度和多时域曲率特征生成在线前瞻指令，在F1TENTH Gym仿真器中使用Stable-Baselines3训练，采用KL惩罚和学习率衰减确保稳定性，随后部署于ROS2环境引导控制器。仿真对比实验表明，所提方法优于固定前瞻和自适应基准；真实小车实验进一步验证了从仿真到硬件的零样本迁移能力。结果表明，该策略在直道增加前瞻距离、弯道减小前瞻距离，有效提升了未见赛道上的圈速表现与重复完成能力。在蒙特利尔赛道达33.16秒，亚斯马林赛道达46.05秒，且能承受更激进的速度剖面，优于所有测试设置。初步实车实验支持仿真实现向硬件的可靠迁移。
原文摘要 · Abstract (English)
Pure Pursuit (PP) is a widely used path-tracking algorithm in autonomous vehicles due to its simplicity and real-time performance. However, its effectiveness is sensitive to the choice of lookahead distance: shorter values improve cornering but can cause instability on straights, while longer values improve smoothness but reduce accuracy in curves. We propose a hybrid control framework that integrates Proximal Policy Optimization (PPO) with the classical Pure Pursuit controller to adjust the lookahead distance dynamically during racing. The PPO agent maps vehicle speed and multi-horizon curvature features to an online lookahead command. It is trained using Stable-Baselines3 in the F1TENTH Gym simulator with a KL penalty and learning-rate decay for stability, then deployed in a ROS2 environment to guide the controller. Experiments in simulation compare the proposed method against both fixed-lookahead Pure Pursuit and an adaptive Pure Pursuit baseline. Additional real-car experiments compare the learned controller against a fixed-lookahead Pure Pursuit controller. Results show that the learned policy improves lap-time performance and repeated lap completion on unseen tracks, while also transferring zero-shot to hardware. The learned controller adapts the lookahead by increasing it on straights and reducing it in curves, demonstrating effectiveness in augmenting a classical controller by online adaptation of a single interpretable parameter. On unseen tracks, the proposed method achieved 33.16 s on Montreal and 46.05 s on Yas Marina, while tolerating more aggressive speed-profile scaling than the baselines and achieving the best lap times among the tested settings. Initial real-car experiments further support sim-to-real transfer on a 1:10-scale autonomous racing platform
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