Efficient Motion Control for Heterogeneous Autonomous Vehicle Platoon Using Multilayer Predictive Control Framework

Abstract

Autonomous driving technology and platooning driving technology are important directions for the development of intelligent and connected vehicles. Aiming at the motion control problem of autonomous vehicle platoon, this article proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. First, the leading autonomous vehicle and following heterogeneous vehicles are modeled, respectively, and the motion control problem of autonomous platoon is described. Then, the multilayer motion control framework is designed, which contains highly automated tracking control optimization for the leading vehicle (LV) and high-precision formation keeping optimization for the following vehicles (FVs). In the upper layer, the heuristic Dyna algorithm-based predictive control (HDY-PC) method is proposed to improve the path tracking performance of the LV. In the lower layer, the improved distributed model-based predictive control (IDM-PC) method is developed to guarantee the motion effectiveness and stability of the vehicle platoon. Besides, the multilayer control framework can handle various communication topologies and dynamic cut-in/cut-out maneuvers. The virtual environment simulation shows that the proposed motion control framework for heterogeneous autonomous vehicle platoon achieves better performance in path tracking and platoon keeping. The adaptability of the framework is also verified using another real-world scene.