Efficient Knowledge-Guided Self-Evolving Intelligent Behavioral Control for Autonomous Vehicles

Abstract

Dear Editor, This letter addresses the enhancement of autonomous vehicles' (AVs) behavior control systems through the application of reinforcement learning (RL) techniques. It presents a novel approach to efficient knowledge-guided self-evolutionary intelligent decision-making by integrating human intervention as prior knowledge into the RL's exploratory learning process. Specifically, we propose an innovative intervention-based reward shaping mechanism and develop a novel experience replay mechanism to augment the efficiency of leveraging guided knowledge within the framework of off-policy RL. The proposed methodology significantly enhances the performance of RL-based behavior control strategies in complex scenarios for AVs. Illustrative results indicate that, relative to existing state-of-the-art methods, our approach yields superior learning efficiency and improved autonomous driving performance.