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A Method to Improve Accuracy of Velocity Prediction Using Markov Model

  • Ya-dan Liu
  • Liang Chu
  • Nan XuEmail author
  • Yi-fan Jia
  • Zhe Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

In order to predict the velocity in driving cycle, first-stage Markov chain (MC) predictor method is adopted. In the traditional Markov prediction model, only one state transition matrix was used to predict the speed. However it will produce a larger error to use the same matrix for predicting speed in different categories of driving cycles. Random Markov-Chain (RMC) model is adopted to improve the accuracy, but the accuracy is still not enough. In this paper, we propose that the state transition matrices in RMC model are divided into two categories: city and highway. Before the prediction, we use the neural network to choose state transition matrix by judging the kinematic parameters of velocity in driving cycles. The simulation results show that the effect of prediction using the state transition matrix after neural network classification is more accurate than no classification. Therefore, the improved RMC model can increase the accuracy of velocity prediction effectively.

Keywords

Neural network Random Markov-Chain model Velocity prediction Classification State transition matrix 

Notes

Acknowledgement

Supported by Jilin Province Science and Technology Development Fund (20150520115JH); Energy Administration of Jilin Province [2016]35.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ya-dan Liu
    • 1
  • Liang Chu
    • 1
  • Nan Xu
    • 1
    Email author
  • Yi-fan Jia
    • 1
  • Zhe Xu
    • 2
  1. 1.State Key Laboratory of Automotive Simulation and ControlJilin UniversityChangchunChina
  2. 2.R&D CenterChina FAW Group CorporationChangchunChina

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