Research on vehicle following before lane changing based on CNN-LSTM model
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-test method is employed to verify the specificity of the motion state of lane changing vehicles before changing lanes.A Convolutional Neural-Long Short Term Memory network(CNN-LSTMnetwork)is built with 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 LSTMnetwork.The LSTMnetwork is employed to predict the following vehicle status.The simulation results show the traditional IDMis 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 IDMmodel,and therefore is more suitable for describing the car following before changing lanes.
car following before lane changevehicle status predictionCNN-LSTM fusion neural networkNGSIM dataset
国家科技期刊平台
NSTL
万方数据
