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Dangerous Driving Behavior Recognition Based on Hand Trajectory

Author

Listed:
  • Wenlong Liu

    (Transportation College, Jilin University, Changchun 130022, China)

  • Hongtao Li

    (Transportation College, Jilin University, Changchun 130022, China)

  • Hui Zhang

    (China FAW Group Corporation Co., Ltd., No. 1, Honaqi Street, Changchun 130013, China)

Abstract

Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.

Suggested Citation

  • Wenlong Liu & Hongtao Li & Hui Zhang, 2022. "Dangerous Driving Behavior Recognition Based on Hand Trajectory," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12355-:d:928188
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    References listed on IDEAS

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    1. Linhong Wang & Hongtao Li & Mengzhu Guo & Yixin Chen, 2022. "The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario," IJERPH, MDPI, vol. 19(3), pages 1-14, February.
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    Cited by:

    1. Osama ElSahly & Akmal Abdelfatah, 2022. "A Systematic Review of Traffic Incident Detection Algorithms," Sustainability, MDPI, vol. 14(22), pages 1-26, November.

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