# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 1 Introduction  
With the continuous development of intelligent transportation travel demand, the real-time prediction of road information has become increasingly important. Neural networks (NNs) provide a new solution for vehicle velocity prediction, which is realized by autonomous learning. The combination of NN and machine learning (ML) in the energy management strategy of the vehicle can predict the road condition information in real-time, which can significantly improve the efficiency of the energy management strategy, improve the engine operating interval, and optimize the energy distribution of the vehicle to reduce the incidence of failure. If the driving state of the vehicle (such as speed and demand torque, etc.) can be predicted in advance, the global optimization algorithm can be used to control the vehicle performance to reach the optimal state within a certain time range [1]. At present, the prediction methods of future vehicle velocity mainly include the prediction method based on exponential function and the prediction method based on driving data [2, 3]. The different methods of vehicle velocity prediction are shown in Figure 1.  
The prediction method based on exponential function represents the future prediction information in the finite time domain in the form of exponential change. When a constant exponential coefficient is adopted, the prediction accuracy of vehicle velocity is low. If the prediction accuracy is improved, the exponential coefficient needs to change according to the different working conditions [4]. [PERSON] et al. [5] studied the prediction accuracy and hybrid electric vehicle (HEV) fuel consumption of the exponential function prediction model. However, this prediction method relies on the assumption of exponential changes in future driving information, and if this assumption is violated, its wide application will be hindered. Prediction methods based on driving data mainly include the Markov chain (MC) model [6; 7; 8] and NN model [9; 10; 11]. The principle is to collect a large number of historical working condition data, establish the corresponding prediction model, and combine the current and historical driving condition information to predict the vehicle velocity. In terms of the MC prediction model, the future vehicle velocity is predicted by the vehicle velocity at the previous moment, which is suitable for the velocity prediction of a fixed driving route. Therefore, this velocity prediction model is more suitable for urban bus driving scenarios due to their repetitive routes [12; 13]. [PERSON] et al. [14] proposed a vehicle velocity prediction method based on the backpropagation neural network (BP-NN); both the acceleration sequence and the velocity sequence as the two inputs to the BP-NN were shown to be effective, while the accuracy of the MC predictor as the benchmark was also low. [PERSON] et al. [15] combined BP-NN and radial basis function neural network (RBF-NN) to predict the vehicle velocity and the future vehicle velocity error at the same time, improving the accuracy of vehicle velocity prediction, and applied it to fuel cell vehicle energy management strategy, reducing the hydrogen consumption by 17.07%. [PERSON] et al. [16], by studying the multivariate correlation between the preview velocity sequence, acceleration sequence, and short-term future velocity sequence and training NN model, found that it could be applied to future velocity prediction in different prediction time horizons. Aiming at the problem of the large cumulative error of the deep neural network (DNN) model, the tolerance sequence correction method was used to transform the setting mechanism, which significantly reduced the impact of prediction error on the system. [PERSON] et al. [17] established a short-term prediction model of driving state by combining stochastic prediction and machine learning. [PERSON] et al.