A Human-Machine Interaction-Experience-Learning Algorithm for Integrated Lateral-Longitudinal Decision-Making and Control in Intelligent Connected Vehicles.

Abstract

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.