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Driving style recognition method using braking characteristics based on hidden Markov model

Author

Listed:
  • Chao Deng
  • Chaozhong Wu
  • Nengchao Lyu
  • Zhen Huang

Abstract

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style.

Suggested Citation

  • Chao Deng & Chaozhong Wu & Nengchao Lyu & Zhen Huang, 2017. "Driving style recognition method using braking characteristics based on hidden Markov model," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0182419
    DOI: 10.1371/journal.pone.0182419
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    Cited by:

    1. Ke Wang & Qingwen Xue & Jian John Lu, 2021. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    2. Santiago Felipe Yepes Chamorro & Juan Jose Paredes Rosero & Ricardo Salazar-Cabrera & Álvaro Pachón de la Cruz & Juan Manuel Madrid Molina, 2022. "Design, Development and Validation of an Intelligent Collision Risk Detection System to Improve Transportation Safety: The Case of the City of Popayán, Colombia," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
    3. Krzysztof J. Szajowski & Kinga Włodarczyk, 2020. "Drivers’ Skills and Behavior vs. Traffic at Intersections," Mathematics, MDPI, vol. 8(3), pages 1-20, March.

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