Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control With PPO
Mohamed Elgouhary, Amr S. El-Wakeel
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jointly</i> chooses the lookahead <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{d}$</tex-math></inline-formula> and a steering gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$g$</tex-math></inline-formula> online using Proximal Policy Optimization (PPO). The policy observes compact state features (speed and curvature taps) and outputs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(L_{d},g)$</tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(L_{d},g)$</tex-math></inline-formula> 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.
BibTeX
@inproceedings{ral2026_learningtotunepu,
title = {Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control With PPO},
author = {Mohamed Elgouhary and Amr S. El-Wakeel},
booktitle = {RA-L 2026},
year = {2026}
}