关键词: 智能交通, 期望编队状态, 模型预测控制, 车辆编队, 分布式优化

Abstract: Traditional studies on vehicle platooning mainly focused on vehicle interactions and the analysis of platoon formation, lacking a systematic exploration of the platoon formation from an overall state perspective. Therefore, this study investigates the optimization mechanism of the vehicle platooning process under the constraint of the overall desired state, and proposes a longitudinal platoon control optimization method based on desired platoon state constraints. First, the desired platoon state is defined, covering five dimensions: platoon completion time, distance, speed, acceleration, and spacing. Then, in the ideal scenario where no vehicles interfere with the leading vehicle, a platoon control optimization model without the front vehicle constraint is established based on model predictive control. Further, for the common scenario where the front vehicle is interfering, a corresponding platoon control model is proposed. This study adopts a distributed computation mode based on adjacent vehicle pairs, which reduces computational pressure while enhancing the safety and robustness of the platooning process. Finally, a Python based visualization simulation program is developed to verify the effectiveness of the algorithm. The results show that excessively short desired distances and expected times are the main factors hindering platoon feasibility, and as the desired distance and speed increase, the minimum feasible platoon time threshold also increases accordingly. In terms of execution, the actual performance of the platoon under feasible desired states is satisfactory, with the deviation from the initial platoon target being less than 1‰ , while ensuring the safety and smoothness of the vehicle trajectories. In terms of computational efficiency, the distributed strategy outperforms the centralized strategy, with this advantage becoming more pronounced as the number of platooning vehicles increases. In the larger-scale platooning task consisting of 9 vehicles, the maximum computation time does not exceed 0.3 seconds.

Key words: intelligent transportation, expected platoon state, model predictive control, vehicle platooning, decentralized optimization