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

doi:10.1109/JAS.2024.124746

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

2025-07-01

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DOI：10.1109/JAS.2024.124746

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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.

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

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