【摘要】 为安全、高效地控制智能网联汽车（CAV）编队，研究了基于多智能体系统（MAS）一致性的多车道、零散随机分布的车辆集群编队控制策略，并进行数值仿真验证。建立了基于交错式结构的车群期望几何拓扑，提出了包括间距调整阶段、变道控制阶段和队形收敛阶段的3阶段式编队控制流程。基于一致性理论，设计了车群编队控制器，并进行稳定性论证。选取2种典型场景，对三车道场景中初始分布较为极端的车群开展编队研究。结果表明：在该策略的控制过程中，纵向车间距与期望间距误差在0.5 m以内，横向位置与期望位置误差在5 cm以内，且速度在变化后能快速收敛至期望值。因而，该策略能有效控制车群车辆安全、高效地达到车群期望队形。更多还原

【Abstract】 A control strategy with multi-lane vehicle platoon formation was investigated for ConnectedAutomated-Vehicles(CAV) based on Multi-Agent System(MAS) consensus to solve the problem of safe and efficient formation control of multi-lane, randomly distributed vehicle clusters. A platoon’s desired geometric topology was established based on an interleaved structure for the vehicle cluster with multiple lanes and scattered random distribution. A three-stage formation control process was proposed, including the spacing adjustment stage, lane change control stage, and formation convergence stage. A platoon formation controller was designed based on consensus theory, and the stability of the controller is demonstrated. Formation experiment of vehicle clusters was conducted in extreme initial distribution scenarios within a three-lane environment, with two typical scenarios being selected to perform numerical simulations and vehicle dynamics simulations of the formation process. The results show that for highly uneven and irregularly distributed vehicle clusters on multiple lanes, all vehicles are effectively controlled to achieve the desired formation in a safe and efficient manner. Throughout the control process, the longitudinal distance error between vehicles and the desired spacing is within 0.5 m, and the lateral position error relative to the desired position is within 5 cm, and the velocity quickly converges to the desired value after changes. Therefore, the strategy can effectively control the vehicles of the cluster to safely and efficiently achieve the desired formation.更多还原