Research on short-time speed prediction based on WSO-optimized CNN-BiLSTM
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.
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