Deep Reinforcement Learning Car-Following Model Considering Longitudinal and Lateral Control

Abstract

The lateral control of the vehicle is significant for reducing the rollover risk of high-speed cars and improving the stability of the following vehicle. However, the existing car-following (CF) models rarely consider lateral control. Therefore, a CF model with combined longitudinal and lateral control is constructed based on the three degrees of freedom vehicle dynamics model and reinforcement learning method. First, 100 CF segments were selected from the OpenACC database, including 50 straight and 50 curved road trajectories. Afterward, the deep deterministic policy gradient (DDPG) car-following model and multi-agent deep deterministic policy gradient (MADDPG) car-following model were constructed based on the deterministic policy gradient theory. Finally, the models are trained with the extracted trajectory data and verified by comparison with the observed data. The results indicate that the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model are both safer and more comfortable than the human-driven vehicle (HDV) on straight roads and curved roads. Under the premise of safety, the vehicle under the control of the MADDPG model has the highest road traffic flow efficiency. The maximum lateral offset of the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model in straight road conditions is respectively reduced by 80.86% and 71.92%, compared with the HDV, and the maximum lateral offset in the curved road conditions is lessened by 83.67% and 78.95%. The proposed car following model can provide a reference for developing an adaptive cruise control system considering lateral stability.