Reinforcement Learning-Based Approach to Reduce Velocity Error in Car-Following for Autonomous Connected Vehicles

This paper suggests an adaptive car-following strategy for autonomous connected vehicles (ACVs) that integrates a robust controller with an extended disturbance estimator (EDE) and reinforcement learning (RL) to improve performance in dynamic traffic environments. Traditional car-following methods struggle to handle external disturbances and uncertainties in vehicle dynamics. The suggested method addresses this by dynamically adjusting the EDE gain using RL, enabling the system to optimize its control strategy in real time continuously. Simulations were conducted in two scenarios, a single following vehicle and two following vehicles, each tracking a leading vehicle. Results showed significant improvements in velocity tracking, with the RL-based control method reducing velocity error by over 50% compared to conventional approaches. The technique also led to smoother acceleration control, enhancing stability and driving comfort. Quantitative metrics, such as total reward, velocity error, and acceleration magnitude, indicate that the suggested EDE-RL-based strategy provides a robust and adaptable solution for autonomous vehicle control. These findings indicate that RL, combined with robust control, can improve the performance and safety of ACV systems, making it suitable for broader applications in autonomous vehicle platooning and complex traffic scenarios, including vehicle-to-vehicle (V2V) communication.