An Efficient Deep Reinforcement Learning-Based Car-Following Method via Rule-Constrained Data Augmentation

This paper introduces a data augmentation method based on control theory and rule constraints, aiming to address the insufficient coverage issue in existing vehicle trajectory datasets. Grounded in control theory, the method combines the motion and safety rules of driving vehicles for data augmentation. It exhibits high interpretability, credibility, a minimal and controllable set of parameters, while maintaining physical feasibility. Additionally, a one-step Markov Decision Process is introduced to model the car following task as a short-term decision problem, adapting to unpredictable variations in the generated trajectories. Experimental results demonstrate that the proposed rule-constrained data augmentation method significantly enhances the model's performance in addressing car following issues, showcasing strong adaptability and generalization capabilities.