![[2602.18386] Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18386] Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO
Summary
This article presents a reinforcement learning approach to optimize Pure Pursuit parameters in autonomous racing, enhancing path tracking performance through joint control of lookahead distance and steering gain.
Why It Matters
As autonomous racing technology evolves, optimizing control strategies is crucial for improving performance across various tracks and conditions. This research demonstrates how reinforcement learning can effectively tune key parameters, potentially leading to advancements in both racing and broader autonomous vehicle applications.
Key Takeaways
- Reinforcement learning can optimize Pure Pursuit parameters for better performance.
- The proposed method outperforms traditional fixed and adaptive control strategies.
- Jointly tuning lookahead distance and steering gain enhances path tracking accuracy.
Computer Science > Robotics arXiv:2602.18386 (cs) [Submitted on 20 Feb 2026] Title:Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO Authors:Mohamed Elgouhary, Amr S. El-Wakeel View a PDF of the paper titled Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO, by Mohamed Elgouhary and Amr S. El-Wakeel View PDF HTML (experimental) 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 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 evaluate...