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Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks

Author

Listed:
  • Lili Zheng

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Yanlin Zhang

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Tongqiang Ding

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Fanyun Meng

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Yanlin Li

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Shiyu Cao

    (Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

Abstract

Driver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction. Based on naturalistic driving study data, distraction risk levels are classified using the driver’s gaze and secondary driving tasks in this paper. The classification results are then combined with road environment factors for accident occurrence prediction. Two ways are suggested to classify driver distraction risk levels in this study: one is to divide it into three levels based on the driver’s gaze and the AttenD algorithm, and the other is to divide it into six levels based on secondary driving tasks and odds ratio. Random Forest, AdaBoost, and XGBoost are used to predict accident occurrence by combining the classification results, driver characteristics, and road environment factors. The results show that the classification of distraction risk levels helps improve the model prediction accuracy. The classification based on the driver’s gaze is better than that based on secondary driving tasks. The classification method can be applied to accident risk prediction and further driving risk warning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4806-:d:1006514
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    References listed on IDEAS

    as
    1. 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.
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