Hybrid Path Tracking Control for Autonomous Trucks: Integrating Pure Pursuit and Deep Reinforcement Learning With Adaptive Look-Ahead Mechanism
Zhixuan Han, Peng Chen, Bin Zhou, Guizhen Yu
Abstract
Path tracking control is essential for ensuring the safe and efficient operation of autonomous trucks, but traditional methods often struggle with nonlinear vehicle dynamics. While deep reinforcement learning (DRL) approaches are model-free, they may lack the stability and interpretability required for reliable deployment. This study presents a hybrid control framework that combines Pure Pursuit (PP) with Proximal Policy Optimization (PPO) to enhance tracking accuracy and robustness. PP provides baseline stability and interpretability, while PPO refines control actions by optimizing policy gradients, ensuring better adaptability to nonlinear dynamics and complex driving conditions. An adaptive look-ahead mechanism, responsive to speed and curvature, dynamically adjusts preview distances using PPO-generated coefficients, facilitating early corrections during high-speed turns and enabling greater precision on sharp curves. A fusion training method, leveraging high-reward initialization and a decreasing learning rate, supports efficient exploration and stable convergence. The approach was validated in a high-fidelity simulation environment using PreScan, Simulink, and ROS, along with real-world experiments on a proportionally scaled intelligent vehicle chassis, demonstrating notable improvements in path tracking accuracy and robustness across varied path profiles.