重庆理工大学学报2024，Vol.38Issue(19)：38-47,10.DOI:10.3969/j.issn.1674-8425(z).2024.10.005
考虑前车信息的CNN-BiLSTM的短时车速预测
Research on short-time speed prediction based on WSO-optimized CNN-BiLSTM
摘要
Abstract
Accurate prediction of vehicle speed is of vital importance for vehicle safety and control.In this paper,a Convolutional Neural Networks(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)based vehicle speed prediction model considering the following vehicle information is proposed.And the White Shark Optimisation(WSO)algorithm is introduced to optimize the hyperparameters of the model.With thorough consideration of the information of the front vehicle and other factors affecting the driving speed when following a vehicle,the relevant data are collected through the driver-in-the-loop platform,and six variables(accelerator pedal opening,brake pedal opening,self-vehicle speed,relative vehicle distance,relative vehicle speed,and self-vehicle acceleration)are determined as inputs to the WSO-CNN-BiLSTM model.The number of modes for the variational modal decomposition is determined by the sample entropy value of the data for noise reduction of the data.Our simulation results indicate the multi-input prediction model considering the information of the front vehicle improves the prediction accuracy compared to the single-input prediction.Compared to SVR(Support Vector Regression),LSTM,CNN,and TCN(Temporal Convolutional Network),it reduces the RMSE values by 63.39％,11.45％,58.45％and 42.58％and cuts the MAE values by 59.09％,8.09％,57.29％,and 38.99％respectively,markedly improving the accuracy of vehicle speed prediction.关键词
车速预测/前车信息/变分模态分解/卷积神经网络/双向长短时记忆神经网络Key words
vehicle speed prediction/front vehicles information/variational mode decomposition/CNN/BiLSTM分类
信息技术与安全科学引用本文复制引用
厉成鑫,李美莹,余曼,王姝,赵轩..考虑前车信息的CNN-BiLSTM的短时车速预测[J].重庆理工大学学报,2024,38(19):38-47,10.基金项目
国家自然科学基金项目(52172362,52372375) （52172362,52372375）
陕西省自然科学基金项目(2023-YBGY-122) （2023-YBGY-122）
陕西省科技重大专项项目(2020ZDZX06-01-01) （2020ZDZX06-01-01）
陕西省科技产业链项目(2020ZDLGY16-01,2020ZDLGY16-02,2021ZDLGY12-01) （2020ZDLGY16-01,2020ZDLGY16-02,2021ZDLGY12-01）
