[2603.28625] Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing

Computer Science > Robotics arXiv:2603.28625 (cs) [Submitted on 30 Mar 2026] Title:Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing Authors:Mohamed Elgouhary, Amr S. El-Wakeel View a PDF of the paper titled Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing, by Mohamed Elgouhary and Amr S. El-Wakeel View PDF HTML (experimental) Abstract: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 c...