基于CNN-LSTM的重型载货车侧翻预测  

Rollover Prediction of Heavy-duty Trucks Based on CNN-LSTM

作　　者：高梦涵 冯樱 Gao Menghan;Feng Ying(School of Automotive Engineering,Hubei University of Automotive Technology,Shiyan 442002,China)

机构地区：[1]湖北汽车工业学院汽车工程学院,湖北十堰442002

出　　处：《湖北汽车工业学院学报》2024年第2期6-11,16,共7页Journal of Hubei University Of Automotive Technology

基　　金：中央引导地方科技发展专项(2019ZYYD019)。

摘　　要：针对车辆在运行过程中左右侧车轮垂直载荷难以直接测量的问题,结合卷积神经网络(convolutional neural network,CNN)和长短时记忆神经网络(long short-term memory,LSTM)对横向载荷转移率(lateral load transfer rate,LTR)进行预测。建立重型载货车TruckSim动力学模型,在鱼钩工况、J-Turn工况和双移线工况下采集车辆的侧向加速度、横摆角速度等行驶状态参数。在MATLAB中建立CNN-LSTM模型,利用CNN-LSTM模型的特征提取和时间序列预测功能,对重型载货车LTR值进行预测,并在多种工况下验证CNN-LSTM模型的性能。结果表明:在不同行驶工况、车辆参数及路面条件下,CNN-LSTM模型能够对重型载货车LTR值进行有效预测。To address the difficulty in directly measuring the vertical load of the left and right wheels during vehicle operation,a convolutional neural network(CNN)and a long short-term memory neural network(LSTM)were used to predict the lateral load transfer rate(LTR).By establishing a TruckSim dynamic model for heavy-duty trucks,state parameters of the vehicle under fishhook,J-Turn,and double lane conditions were collected,such as lateral acceleration and yaw velocity.A CNN-LSTM model was established in MATLAB,and the feature extraction and time series prediction functions of the model were used to predict the LTR of heavy-duty trucks.In addition,the performance of the CNN-LSTM model was verified under various working conditions.The experimental results show that the CNNLSTM model can effectively predict the LTR of heavy-duty trucks under different driving conditions,vehicle parameters,and road conditions.

关 键 词：重型载货车 侧翻预测 横向载荷转移率 时间序列预测 

分 类 号：U463.33[机械工程—车辆工程]