# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 5 Conclusions  
In this paper, the kinematic segments of typical driving conditions were taken as samples, a clustering method of multi-dimensional parameter samples based on principal component analysis was proposed, and QPSO-LSTM vehicle velocity prediction sub models were established for three types of samples, respectively. In the process of velocity prediction, the category of current driving conditions is determined by extracting the driving information in the past period of time based on k-means, and the sub model of QPSO-LSTM velocity prediction under this category was used to predict the velocity, and an adaptive velocity prediction method based on kinematic segment recognition was realized.  
* Kinematic segments of typical driving cycles were extracted, and ten characteristic parameters including the average velocity, mean square error of velocity, and maximum velocity were calculated. In order to reduce the computation and avoid the overlap of information, the principal component analysis method was used to reduce the dimension of the eigenvector composed of the eigen parameters. Each principal component is a parameter related to ten features. The three principal components are used to describe the ten feature parameters of each kinematic segment, and the transformation of the feature vector from ten dimensions to three dimensions is realized.  
Figure 17: Comparison of the prediction results of different methods.  
* According to the three principal component information of each kinematic segment sample, the K-means algorithm was used to cluster the samples, and sample sets under three typical working conditions were obtained. These respectively represent the high velocity, long driving mileage type 3, low velocity, long idling time, short driving mileage type 1, and the characteristics between the two driving conditions type 2.
* The vehicle velocity prediction sub-model based on LSTM was trained with three kinds of kinematic fragment samples, respectively, and the optimal LSTM hyperparameters were found by iterative correction with the QPSO algorithm. Combining kinematic segment recognition and a vehicle velocity prediction model, a vehicle velocity prediction method based on K-means-QPSO-LSTM was obtained. This method has an adaptive neural network that can change the vehicle velocity prediction models in real-time according to the actual driving information. Compared with the traditional neural network-based vehicle velocity prediction method, the prediction accuracy was improved.  
The model proposed in this paper provides a theoretical basis for the energy management strategy of hybrid electric vehicles based on vehicle velocity prediction. However, there are still some limitations. How to strike a balance between the prediction accuracy and model complexity is always a difficult problem. The proposed vehicle velocity prediction model has clear advantages in terms of prediction accuracy, however, the time taken to train the deep model is also of concern. In the future, we consider placing the model in a parallel framework such as Apache Spark to improve the efficiency of the model. In addition, the weather factor was not considered in this paper. In future work, we will add more influencing factors and use a space-time deep learning neural network model to improve the prediction accuracy of the vehicle velocity prediction model.  
B.L., Data curation, formal analysis, writing original draft, methodology; C.W., Project administration, resources, supervision, writing editing; F.F., Conceptualization, investigation, writing review. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of Beijing province (Grant No. 4242047).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.