VSPNet: a vehicle speed prediction model incorporating transformer and BiLSTM

Abstract In recent years, with the increasing adoption of hybrid vehicles, energy management strategies have become a prominent research focus. Accurate vehicle speed prediction (VSP) is a critical prerequisite for achieving optimal results in predictive energy management strategies. However, existing speed prediction algorithms fail to fully leverage vehicle data to enhance prediction accuracy. Therefore, a novel VSP Net (VSPNet) is proposed in this study. Firstly, we constructed a combined cycle condition for model training through comprehensive analysis and analyzed the vehicle feature parameters through the random forest algorithm and Pearson correlation analysis to select the best input feature parameters. Then a VSPNet speed prediction model is proposed based on the Transformer model. In the encoder part, firstly, by assigning weights to the input feature parameters and incorporating the temporal attention mechanism, the model is made to make better use of the input features from two dimensions, and at the same time the Transformer model’s encoder based on positional coding combined with Bi-directional Long Short-Term Memory belonging to recurrent neural networks, which is used as a decoder to better catch and handle long-term dependencies in sequence data. Finally, a comparative experiment between VSPNet and the classical speed prediction models was carried out. The proposed VSPNet model reduces the root mean square error (RMSE) by 37%, 22%, and 20% and mean absolute error (MAE) by 39%, 25, and 24% compared to the LSTM model for the prediction time horizons of 3, 5, and 8 s. The RMSE is reduced by 47%, 28%, and 7%, and the MAE is reduced by 47%, 30%, and 9% compared to the Transformer model for the prediction time horizons of 3, 5, and 8 s. The experimental results demonstrate the superiority of this speed prediction model.