关键词: 换道前跟驰, 车辆状态预测, ＣＮＮＬＳＴＭ融合神经网络, ＮＧＳＩＭ数据集

Abstract: Obvious differences exist between the car following before lane change and the car following without lane change.This paper proposes the“car following before lane change”to study the special car following before changing lanes.The lane change is divided into two stages：“basic car following”and“car following before lane change”，with the fifth and eighth Quantile of the slope of the main vehicle before lane change as the end point of“car following before lane change”.Z-testmethod is employed to verify the specificity of themotion state of lane changing vehicles before changing lanes.A Convolutional Neural-Long Short Term Memory network（CNN-LSTM network）is builtwith vehicle speed，acceleration，relative distance and lateral offset as inputs.The CNN layer is employed to extract input layer features，which are then used as inputs to the LSTM network.The LSTM network is employed to predict the following vehicle status.The simulation results show the traditional IDM is not suitable for the special car following behavior before changing lanes.Our CNN-LSTM model improves the acceleration accuracy by 15.1% compared to the traditional IDM model，and therefore is more suitable for describing the car following before changing lanes.