Dynamic lane changing trajectory planning for CAV: A multi-agent model with path preplanning

Abstract

This paper presents a multi-agent dynamic lane-changing (LC) trajectory planning method for CAV. In this method, a decision module is constructed by means of a potential field to determine the LC starting point. Then a series of trajectories is generated in the trajectory generation module. A cost function is constructed for searching for the corresponding optimal trajectory for both the subject vehicle and the participants. The simulation results indicate that the proposed model improves the LC success rate and reduces duration. Differing from the traditional model, we consider the cooperation feature of CAV’s LC and satisfy the subject vehicle’s demand as well as minimizing its impact on the other participants. Moreover, the driving environment including mesoscale information is considered to improve the LC success rate, which provides a new strategy for optimizing LC decision. Additionally, the method can also be applied to simulate CAVs’ LC behaviour.