Accurate identification of driver’s distraction is of great significance to prevent traffic accidents. In this study, the information of driver’s body joint points is fully utilized, and a machine vision-based biomechanical distraction identification method is proposed. A real vehicle driving experiment is designed and organized. The normal driving data and biomechanical distraction data of 20 participants are collected to construct a data set. The Lightweight OpenPose network is trained to extract the position information of the driver’s joint points. A number of characteristic parameters such as the driver’s limb angles or Euclidean distance between the joint points can be calculated based on the position information. The experimental data are preprocessed using moving average filter and factor analysis. The biomechanical distraction identification model is trained with Particle Swarm Optimization (PSO) and Probabilistic Neural Network (PNN), and an accuracy rate of 91.5% is achieved. The possibility of using body joint points to identify the biomechanical distraction is demonstrated in this study. It provides theoretical and technical support for the identification and application of driver’s biomechanical distraction. It is of great significance to improve the active safety performance of smart vehicles.


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    Title :

    A Biomechanical Distraction Identification Method Based on Recognition of Driver’s Joint Points


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wuhong (editor) / Guo, Hongwei (editor) / Jiang, Xiaobei (editor) / Shi, Jian (editor) / Sun, Dongxian (editor) / Wang, Xiaoyuan (author) / Chen, Longfei (author) / Wang, Bin (author) / Shi, Bowen (author) / Wang, Gang (author)

    Conference:

    International Conference on Green Intelligent Transportation System and Safety ; 2022 ; Qinghuangdao, China September 16, 2022 - September 18, 2022



    Publication date :

    2024-09-29


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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