关键词: 车辆工程, 智能交通, 微观交通流, 车辆换道, 贝叶斯网络

Abstract: Lane change behavior is one of the typical driving behaviors in micro traffic flow. Researching vehicle lane change decision-making model can provide a theoretical basis for cooperative adaptive cruise control (CACC) of automatic driving vehicles, and can also effectively reduce the traffic accidents caused by dangerous lane-changing behavior. To make the lane change model more adaptable to the dynamic traffic environment, according to the measured test data of NGSIM project of Federal Highway Administration of the U.S. Department of Transportation, the relevant characteristic parameters such as own vehicle speed, acceleration and relative time distance between vehicles and interactive vehicles during the decision-making of vehicle lane change were analyzed. And by using the theory of Bayesian network artificial intelligence, the decision-making model of vehicle lane change was established, which was compared with the measured data of NGSIM through simulation analysis. The results show that the average decision-making accuracy and recognition rate of the lane change decision-making model based on Bayesian network can reach more than 89%, which has good lane change decision-making effect, and can provide theoretical reference for CACC of intelligent vehicles and depth learning of automatic driving.

Key words: vehicle engineering, intelligent transportation, micro-traffic flow, lane change, Bayesian network