Connected and automated vehicle (CAV) platoons provide significant advantages in enhancing traffic efficiency and safety through vehicle-to-vehicle cooperative driving. However, owing to the uncertainty of human-driven vehicles in mixed traffic environments, platoons must frequently split to avoid potential collisions and merging is required to maintain platoon following. To address this challenge, this paper proposes a cooperative control architecture for CAV platoons that includes a single-vehicle cruising control mode and a platoon-following control mode, enabling independent operation of each mode and discrete event transitions around split and merge maneuvers. In single-vehicle mode, a driving safety potential field model is proposed for collision-avoidance trajectory planning, and a distributed model predictive control algorithm is designed to achieve the distinct control objectives of the two modes. Then, a long short-term memory (LSTM) neural network and fuzzy logic are combined to predict collision risk and determine platoon split events. A cooperative control system is implemented to ensure continuous control and flexible switching between the two modes. Finally, joint simulations in PreScan, CarSim, and MATLAB/Simulink were conducted to evaluate the performance of the system across various obstacle scenarios. The results demonstrate that the proposed control architecture effectively coordinates vehicle maneuvers and adapts platoon formation to changes in traffic conditions.
Han et al. (Sat,) studied this question.