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Vehicle Driving Risk Prediction Based on Markov Chain Model

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  • Xiaoxia Xiong
  • Long Chen
  • Jun Liang

Abstract

A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.

Suggested Citation

  • Xiaoxia Xiong & Long Chen & Jun Liang, 2018. "Vehicle Driving Risk Prediction Based on Markov Chain Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-12, January.
  • Handle: RePEc:hin:jnddns:4954621
    DOI: 10.1155/2018/4954621
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    Cited by:

    1. Lili Zheng & Yanlin Zhang & Tongqiang Ding & Fanyun Meng & Yanlin Li & Shiyu Cao, 2022. "Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks," Mathematics, MDPI, vol. 10(24), pages 1-23, December.
    2. Yunjong Kim & Juneyoung Park & Cheol Oh, 2021. "A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data," Sustainability, MDPI, vol. 13(11), pages 1-15, May.

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