吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 1851-1857.doi: 10.13229/j.cnki.jdxbgxb201606013

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Driving risk status identification based on Markov blanket hidden Naive Bayes

YAN Li-xin1, 2, 3, HUANG Zhen4, ZHU Dun-yao1, 2, CHEN Zhi-jun1, 2, RAN Bin3   

  1. 1.Intelligent Transportation Systems Center, Wuhan University of Technology, Wuhan 430063, China;
    2.National Engineering Research Center for Water Transport Safety, Wuhan 430063, China;
    3.Transportation Engineering Laboratory, University of Wisconsin-Madison, Madison 53705, USA;
    4.School of Automation, Wuhan University of Technology, Wuhan 430070, China
  • Received:2016-03-29 Online:2016-11-20 Published:2016-11-20

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Abstract: In order to effectively identify risk status while driving, a driving risk status identification model is proposed based on the information of driver operation and vehicle status. According to the rate of electrocardiogram (ECG) and traffic violation behaviors, the driving risk status is classified into four levels. Using Markov blanket algorithm, five factors are selected as the feature set, including the speed, the longitudinal acceleration, the rate of front wheel angle, the vehicle position and lane departure. Then, the algorithm of Hidden Naive Bayes (HNB) is employed to establish the driving risk status identification model. The results show that the accuracy of HNB is 90.6%, increasing 14.1%, 13.9 and 13.0% compared with Bayesian Network (BN), Naive Bayes (NB) and Radial Basis Function Neural Network, respectively. The results of ROC curve indicates that the model presents high predictive power. The conclusions can provide theoretical support for designing dangerous driving status recognition equipment based on vehicle and driver operation information.

Key words: road engineering, risk status identification, Markov blanket, Hidden Naive Bayes, traffic safety

CLC Number: 

  • U491
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