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

📰 ArXiv cs.AI

Researchers propose a hybrid control framework using Proximal Policy Optimization (PPO) to dynamically adjust lookahead distance in Pure Pursuit for autonomous racing

advanced Published 31 Mar 2026

Action Steps

Implement Proximal Policy Optimization (PPO) algorithm to learn optimal lookahead distance policies
Integrate PPO with Pure Pursuit path-tracking algorithm
Train the hybrid control framework using reinforcement learning to adapt to various racing scenarios
Evaluate and fine-tune the framework for improved performance and stability

Who Needs to Know This

This research benefits autonomous vehicle engineers and researchers who can apply the hybrid control framework to improve path-tracking performance, and machine learning engineers who can utilize reinforcement learning techniques for dynamic control problems

Key Insight

💡 Using reinforcement learning to dynamically adjust lookahead distance can improve path-tracking performance in autonomous vehicles