Distributed imitation learning and adversarial path tracking strategies for intelligent connected vehicles oriented to dynamic interaction scenarios

In complex and dynamic traffic environments, factors such as multi-agent interaction behaviors, random obstacle disturbances, and communication delays continuously increase the difficulty of path tracking and cooperative control for intelligent connected vehicles. A collaborative autonomous driving control model integrating distributed imitation learning and adversarial path tracking mechanism is constructed to address issues such as accumulated path offsets, insufficient decision stability, and reduced generalization ability in complex environments in dynamic interaction scenarios. First, the information is uniformly encoded through the expert driving behavior enhancement and multi-source heterogeneous data fusion method. The spatio-temporal interaction feature learning network based on the multi-head attention mechanism is then constructed to model the dynamic correlations among vehicles, pedestrians and obstacle targets, and combined with the value network - policy network joint control structure to achieve path tracking decision optimization. The study further introduces adversarial perturbation training and action smoothing constraints to enhance the model’s robust path tracking ability and stable control performance under conditions of communication delay, random noise, and dynamic obstacle perturbation. Experimental results show that the path tracking error of the proposed model is only 0.18 m, the average lateral offset is controlled at 0.12 m, the collision rate is reduced to 2.8%, the task completion rate is 96.4%, and the stability control rate is 92.6%. Meanwhile, the model had an average decision response time of only 48.6ms, coordinated traffic efficiency of 81.7%, and a comprehensive robustness score of 0.93. The results show that distributed imitation learning and adversarial path tracking mechanisms can effectively improve the path tracking accuracy, collaborative decision-making ability and long-term operational stability of intelligent connected vehicles in complex dynamic interaction environments.