Research on Multi-vehicle Coordinated Lane Change of Connected and Automated Vehicles on the Highway 2019-01-0678
With the rapid development of modern economy and society, traffic congestion has become an increasingly serious problem. Vehicle cooperative driving can alleviate traffic congestion and improve road traffic capacity. Compared with vehicle separate control, cooperative driving combines various vehicle systems, and highly integrates information on obstacle location, vehicle status and driving intention. Then the controller uniformly issues instructions to ensure the orderly driving of the platoon. In the cooperative driving platoon, the displacement difference and the speed difference between vehicles have a certain relationship, which reduces the possibility of traffic accidents and then improves the safety of driving. In the process of cooperative driving, if there are multiple vehicles whose speeds don’t meet the current lane requirements, or if there are obstacles ahead, multi-vehicle lane change measures must be taken. This paper establishes a vehicle system based on "human-vehicle-environment-task" generalized mechanical dynamics system and multi-objective coordinated control, which solves the problem of long distance and low efficiency in the process of lane change. The strategy proposed can also slow down the lateral and longitudinal impact of passengers and reduce the fuel consumption. In view of the above problems, the article firstly constructs a kinematic model that accurately characterizes the behavior mechanism of intelligent vehicles. Next, the lateral and longitudinal coordinated control method is proposed, and trajectory tracking is realized based on MPC algorithm. Finally, the control performance is verified by PreScan software, and the stability and robustness of the collaborative control system are analyzed. This paper solves the problem of multi-objective collaborative control that comprehensively considers intelligent driving safety, passenger comfort and lane change efficiency. The results have important research significance for improving the safety, comfort, energy saving and environmental protection performance of intelligent vehicles and traffic environments.
Di Liu, Jian Wu, Yifan Ye, Jiangchao Shi, Yuan Wang