重庆理工大学学报2024，Vol.38Issue(3)：1-8,8.DOI:10.3969/j.issn.1674-8425(z).2024.02.001
基于CNN-LSTM模型的车辆换道前跟驰研究
Research on vehicle following before lane changing based on CNN-LSTM model
摘要
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-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.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Key words
car following before lane change/vehicle status prediction/CNN-LSTM fusion neural network/NGSIM dataset分类
交通运输引用本文复制引用
潘公宇,马斌..基于CNN-LSTM模型的车辆换道前跟驰研究[J].重庆理工大学学报,2024,38(3):1-8,8.基金项目
国家自然科学基金面上项目(52072157) （52072157）
