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• A hybrid control framework integrating Proximal Policy Optimization (PPO) with the classical Pure Pursuit controller can dynamically adjust the lookahead distance during autonomous racing.method 95%
• The proposed reinforcement learning-based Pure Pursuit controller improves lap-time performance on unseen tracks compared to fixed-lookahead and adaptive heuristic baselines.result 90%
• The learned controller adapts the lookahead distance by increasing it on straights and reducing it in curves.result 85%
• The proposed method tolerates more aggressive speed-profile scaling than the baseline controllers.result 80%
• The learned policy transfers zero-shot from simulation to a real 1:10-scale autonomous racing platform.result 90%
• A Pure Pursuit controller with a fixed lookahead distance is highly sensitive to the map and speed profile, requiring extensive retuning for each new map.limitation 95%
• The PPO agent uses vehicle speed and multi-horizon curvature features as observations to learn a dynamic mapping to the lookahead distance.method 95%
• The training process for the PPO agent used a KL penalty and learning-rate decay for stability.technical 90%

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