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Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory

Author

Listed:
  • Min Li

    (School of Mechanical and Automobile Engineering, Qingdao University of Technology, No. 777 Jialingjiang Road, Qingdao 266520, China)

  • Wuhong Wang

    (Department of Industrial Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Zhen Liu

    (Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China)

  • Mingjun Qiu

    (China National Heavy Machinery Research Institute Co., Ltd., No. 3699 Shanglin Road, Xi’an 710032, China)

  • Dayi Qu

    (School of Mechanical and Automobile Engineering, Qingdao University of Technology, No. 777 Jialingjiang Road, Qingdao 266520, China)

Abstract

Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.

Suggested Citation

  • Min Li & Wuhong Wang & Zhen Liu & Mingjun Qiu & Dayi Qu, 2022. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory," Sustainability, MDPI, vol. 14(11), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6901-:d:832185
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    References listed on IDEAS

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    1. Saeed Solaymani & Saeed Sharafi, 2021. "A Comparative Study between Government Support and Energy Efficiency in Malaysian Transport," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    2. Alessandro Severino & Giuseppina Pappalardo & Salvatore Curto & Salvatore Trubia & Isaac Oyeyemi Olayode, 2021. "Safety Evaluation of Flower Roundabout Considering Autonomous Vehicles Operation," Sustainability, MDPI, vol. 13(18), pages 1-14, September.
    3. Yongfeng Ma & Xin Gu & Ya’nan Yu & Aemal J. Khattakc & Shuyan Chen & Kun Tang, 2021. "Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    4. Elżbieta Macioszek, 2020. "Roundabout Entry Capacity Calculation—A Case Study Based on Roundabouts in Tokyo, Japan, and Tokyo Surroundings," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    5. Zhensheng Wang & Yang Yue & Qingquan Li & Ke Nie & Wei Tu & Shi Liang, 2017. "Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
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