# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 1 Introduction
[18] used the global positioning system to obtain the drivers' historical driving data, predict and identify driving routes, and use dynamic programming (DP) algorithms to achieve the optimal battery state adjustment of vehicle charging consumption. [PERSON] et al. [19] combined traffic signals and passenger numbers with energy management strategies based on deep reinforcement learning and used a large number of driving cycles generated by training traffic simulations for training. A NN-based travel model was proposed to help manage the energy distribution of PHEVs, where the simulation results verified that this model could improve the prediction accuracy and contribute to improving fuel economy [20]. Nevertheless, the predictive performance of the NN model is easily affected by its fixed network structure, learning rate, and input. Figure 1: Different vehicle velocity prediction methods. In addition, the evaluation metrics and the amount of training data collected also affect the accuracy of the model. [PERSON] et al. [21] applied the long short-term memory network (LSTM) to the traffic field for the first time and verified the advantages of LSTM in prediction accuracy by comparing it with many traditional prediction methods. In addition, convolutional neural networks (CNNs) have been used in the prediction research of road network traffic flow and combined with deep neural networks (DNNs) to capture the temporal and spatial characteristics of traffic flow [22; 23; 24]. In the above vehicle velocity prediction methods, most researchers have built a unique vehicle velocity prediction model to predict the future velocity sequence. However, the number of neurons, the learning rate, batch size, diffusion factor, hidden layer number, epochs, and other parameters in the vehicle velocity prediction model have a wide range of values, and the same parameter will show a different prediction accuracy in different cycle conditions. Therefore, a single predictive model is not sufficient to describe all driving processes. In order to build a more accurate model, we need to decompose the prediction model into multiple sub-prediction models corresponding to different parameter intervals. Moreover, the type of driving condition of a vehicle is a complex variable that is determined by many factors, which will have a significant impact on vehicle performance. Correctly distinguishing the driving condition types will help to decompose the unified model into a series of sub-neural network models corresponding to different driving condition types to improve the accuracy of the prediction model. Based on the above analysis, the same driving conditions often have similar velocity fluctuation trends. In order to further improve the prediction accuracy of vehicle velocity, a hybrid model of clustering data with LSTM-NN was proposed. Firstly, kinematic fragment samples in typical working conditions were extracted and feature parameters were calculated. Principal component analysis was used to reduce the dimensionality of feature parameters with multiple dimensions, and samples with similar characteristics were clustered by the K-means algorithm. The clustering results could effectively express the historical correlation and trend correlation of vehicle velocity in various working conditions. Then, the LSTM model was trained by using the training data obtained by clustering, the number of neurons, training times, and learning rate of LSTM-NN were optimized by quantum particle swarm optimization algorithm (QPSO), and the K-means-QPSO-LSTM prediction model was constructed for prediction. Finally, a China Heavy-duty commercial vehicle Test Cycle-Tractor Trailer (CHTC-TT) was used as the test cycle. The hybrid prediction model based on K-means-QPSO-LSTM was compared with radial basis function (RBF)-NN and CNN-LSTM, which are commonly used in vehicle velocity prediction.