Lane Change Decision Based on the Safety State Division of the Adjacent Lanes

doi:10.3901/JME.2023.02.233

Abstract

Abstract: According to the shortcoming that lane change safety is difficult to ensure due to insufficient analysis of the traffic state of the adjacent lane in most lane change decision studies, a lane change decision method based on the safety state division of the adjacent lane is proposed to solve this problem. Based on the vehicle-vehicle relative position relationship, the classification criteria for associated vehicles is established to determine the vehicle that need to be monitored for lateral and longitudinal motion in the current lane, adjacent lanes, and adjacent second lanes. A deep neural network and a lateral deviation judgment standard based on the difference between left and right lane changes are designed to predict the lateral movement behaviors of lane keeping, lane departure and lane change for the associated vehicles. Considering the influence of different lateral motion behaviors of related vehicles on the security situation of the adjacent lanes of the vehicle and the relative longitudinal motion of the vehicle-vehicle, the safety state division method of the adjacent lanes is designed to determine the security level of the adjacent lanes. Then, the lane change decision criterion is designed to realize the precise decision-making for lane change. The simulation results show that the proposed lane change decision method can accurately predict the lateral motion behavior of associated vehicles, and can achieve more accurate lane change decision for different driving environments, and improve lane change safety of the vehicle. And the effectiveness of the method is validated through the real vehicle experiments.

Key words: adjacent lanes, safety state, lateral movement behavior, lane change decision

CLC Number:

U461

WANG Huiran, CHEN Wuwei, WANG Qidong, ZHAO Linfeng, ZHU Maofei, WEI Zhenya. Lane Change Decision Based on the Safety State Division of the Adjacent Lanes[J]. Journal of Mechanical Engineering, 2023, 59(2): 233-244.

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