Abstract
The emerging technology of vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication makes it possible for vehicles to sense the environment information, which can be exploited to assist the vehicle in cooperative motion planning. In this paper, we focus on the cooperative trajectory planning of lane changes for connected and automated vehicles (CAVs). The proposed model considers the traffic scene with multiple mandatory lane change demands and completes the trajectory planning for vehicles by taking the safety and efficiency into consideration. The model solves two critical issues: the vehicle grouping and the motion planning. In the first issue, CAVs in the cooperative zone are divided into different groups. Then the problem is simplified and divided into several subproblems. In the second issue, the trajectory planning is conducted in each group. Trajectories are generated for vehicles with and without lane change demands. Besides, these two steps are iterated and updated in the fixed time interval, which makes full use of the dynamic cooperation ability of vehicles. Extensive simulation tests are conducted to validate the performance of the model. Results show that the cooperation of vehicles realizes safe and effective lane changes.
Highlights
The lane change is one of the common and essential operations of vehicles, which has significant influence on the traffic safety and efficiency [1]
This paper focuses on the problem of multi-vehicle cooperative lane change maneuvers for connected and automated vehicles (CAVs)
The main contribution of this paper is to develop a dynamic cooperative planning model for lane changes of autonomous driving. It divides the traffic flow into groups which simplifies the problem. Another advantage of the model over other models is that it allows vehicles to cooperate with others, which means that vehicles accelerate and decelerate to create the proper gap for the mandatory lane change operation
Summary
TINGTING LI 1, JIANPING WU1, CHING-YAO CHAN2, MINGYU LIU1, CHUNLI ZHU1, WEIXIN LU3, AND KEZHEN HU4.
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