Abstract
Considering the inaccuracies of the traditional Hidden Markov Model (HHM) in the dynamic processes that are close relatively related before and after characterization, an autoregressive state prediction model based on Hidden Markov with Autoregressive model and the coefficient of AR is proposed, which takes the coefficient of AR as the observations of the continuous HHM. Taking the recognition and prediction of heavy vehicle driving states as the research object, a two-layer HMM model is set up to describe the state of the whole steering process of the vehicle. The AR model is for the features extracting of the observations in a short period of time, and the coefficient of AR is extracted as the observed sequence of the lower HMM model library. The upper HMM is used to identify and predict the overall state of the vehicle during steering. The proposed model makes the state sequence with the highest probability on-line predicted in the observed sequence by the Viterbi algorithm, and calculates the state transition law to predict the state of the vehicle in a certain period of time in the future using the Markov prediction algorithm. Combining the double lane change and hook steering to train the parameters of the model, the online identification and prediction of heavy vehicle rollover states can be achieved. The results show that the proposed model can accurately identify the driving state of the vehicle with good real-time performance, and the good prediction on the trend of heavy vehicle driving conditions is verified.
Similar content being viewed by others
References
Zhao, Z., Wang, Y., Hu, X., et al. (2017). Research on identification method of heavy vehicle rollover based on Hidden Markov model. Open Physics, 15(1), 479–485.
Huang, H., Yang, M., Wang, C., et al. (2015). Collision warning system based on forward vehicle behavior recognition. Journal of Huazhong University of Science and Technology (Natural Science Edition), 43, 117–121.
Zhu, T., Kong, X., & Li, B. (2015). Research on driving status recognition of heavy duty vehicle based on double-layer HMM model. Acta Arm Amentarii, 36(10), 1832–1840.
Xiao, X., Ren, C., & Wang, Q. (2013). Research on driving behavior prediction method based on HMM. China Mechanical Engineering, 24(21), 2972–2976.
Fan, J., Ruan, T., Wu, J., et al. (2016). Vehicle behavior recognition method based on quadratic spectral clustering and HMM-RF hybrid model. Computer Science, 43(5), 288–293.
Song, X., Zheng, Y., & Cao, H. (2016). Research on driver’s lane change intention recognition based on HMM and SVM. Journal of Electronic Measurement and Instrumentation, 30(1), 8–65.
Zhao, Z., & Wang, D. (2013). Research status and development trend of side tumbling pre-warning technology of heavy vehicle. Journal of Hebei University of Science and Technology, 34(2), 108–112.
Nie, Z., & Zong, C. (2015). A study on the parameters identification of simplified models for articulated heavy vehicles. Automotive Engineering, 37(6), 622–630.
Wang, X., Cong, Z., Fang, L., et al. (2013). Determination of Real-time Vehicle Driving Status Using HMM. Acta Automatica Sinica, 39(12), 2131–2142.
Sun, R., Fan, H., & Zhao, G. (2017). Intelligent prediction model for nonlinear time series based on reinforcement learning. Journal of Dalian Maritime University, 43(4), 97–103.
Cui, J., Gao, B., Jiang, L., et al. (2017). Application research of LSSVM and HMM in aeroengine condition prediction. Computer Engineering, 43(10), 310–315.
Acknowledgments
The authors acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51675212, 51505173), Major Project of Natural Science Research in Universities of Jiangsu Province (Grant No. 16KJA460004), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX17_0703).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhao, Z., Wang, Y., Feng, M. et al. Autoregressive State Prediction Model Based on Hidden Markov and the Application. Wireless Pers Commun 102, 2403–2416 (2018). https://doi.org/10.1007/s11277-018-5259-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5259-7