A Human-Machine Interaction-Experience-Learning Algorithm for Integrated Lateral-Longitudinal Decision-Making and...

Traditional reinforcement learning for autonomous driving faces challenges such as slow convergence, control instability, and limited adaptability. To address these issues, this paper proposes a hybrid decision-making framework that integrates V2X communication, human-machine interaction experience, and deep reinforcement learning. Based on the Actor-Critic architecture, the framework incorporates a human-like driving tendency network to constrain the policy search space and integrates a Kalman filter-based V2X communication anomaly handling module. The model constructs the vehicle's action space and state representation. Comparative experiments with a method lacking the driving tendency module and the hierarchical planning method DDPG-RRT* show that DDPG-HP achieves faster convergence and better scenario adaptability than these baselines. It effectively avoids static and dynamic obstacles, demonstrates anticipatory and gradual deceleration, and initiates braking 1.7 s earlier than the DDPG-RRT* method, thereby reducing collision risks and enhancing pedestrian safety. Furthermore, the communication anomaly compensation mechanism effectively improves the system's adaptability and overall robustness under unstable communication conditions.