Lane-Changing Tracking Control of Automated Vehicle Platoon Based on MA-DDPG and Adaptive MPC

To address the problem of autonomous lane-changing maneuvers for automated vehicle platoons on highways, a novel platoon lane-changing (PLC) tracking control framework based on the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) and adaptive model predictive control (AMPC) is presented. Currently, the classic platoon cooperative control method is complex in structure and necessitates the establishment of an accurate vehicle dynamics model, so a fully decentralized MA-DDPG algorithm is proposed to realize the longitudinal following control, handling nonlinear systems and continuous state space. The agents associated with each following vehicle stay in communication via the predecessor-leader following (PLF) or predecessor following (PF) communication topology, training locally. Secondly, the traditional MPC is prone to large fluctuations in the early stage of solving and its tracking performance deteriorates with varied longitudinal speed. Therefore, an AMPC controller whose prediction time domain changes with the longitudinal speed is combined with the quintic polynomial curve to complete the lateral control, and a fuzzy controller is introduced for compensation of front wheel steering angles. The results of the joint Carsim/Simulink simulation demonstrate that the AMPC controller can achieve lateral tracking control under working conditions of 50 km/h, 100 km/h, and variable speed, and the front wheel steering angles are also stable. The MA-DDPG algorithm can achieve effective following control when the longitudinal expected speed is 25 m/s. The framework proposed in this study can both track effectively and smooth the longitudinal velocity profile compared with algorithm combinations 1: CACC with AMPC and 2: CACC with PID.