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GE Hui-min, ZHENG Ming-qiang, LYU Neng-chao, LU Ying, SUN Hui. Review on driving distraction[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 38-55. doi: 10.19818/j.cnki.1671-1637.2021.02.004
Citation: GE Hui-min, ZHENG Ming-qiang, LYU Neng-chao, LU Ying, SUN Hui. Review on driving distraction[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 38-55. doi: 10.19818/j.cnki.1671-1637.2021.02.004
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Review on driving distraction

doi: 10.19818/j.cnki.1671-1637.2021.02.004
  • 1.

    School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

  • 2.

    Intelligent Transportation System Research Center, Wuhan University of Technology, Wuhan 430070, Hubei, China

Funds:

National Natural Science Foundation of China 51905224

National Natural Science Foundation of China 51605197

More Information
  • Author Bio:

    GE Hui-min(1979-), female, associate professor, PhD, hmge@ujs.edu.cn

  • Received Date: 2020-09-10
  • Publish Date: 2021-04-25
    Abstract
  • An indicator system for evaluating the quality of literatures was established. Based on this system and considering driving behavior as the main focus of this research, 288 relevant papers were selected, and their data acquisition methods, indicator selections, detection methods, and research conclusions were comprehensively analyzed. Taking driving behavior as the main research object, a method of obtaining test data on driving distractions was systematically derived combined with statistical methods, and the reasons for the diversity and polarization in the obtained data were summarized. The research results of different driving distraction indicators were categorized, and the efficiency, advantages, and disadvantages of these indicators were summarized. The accuracies of different driving distraction detection models were compared, and the root causes of their differences were analyzed. Future research trends of driving distraction data acquisition methods, indicator selections, and detection methods were proposed. Analysis results show that experimental tests are the primary methods for obtaining driving distraction data. Natural driving datasets and video recordings have been proposed as new methods of data acquisition, data acquisition methods of roadside observations and surveys have received less attention. Comparison scenario, vehicle following scenario, overtaking scenario, lane changing scenario, and relatively more complex scenarios involving other dangerous events are the most extensively studied driving distraction scenarios. The setting of driving distraction sub-tasks indicates that current research on driving distraction has focused on several types and topics. Fusion indicators, generally including driving performance and eye movement indicator, and driving performance and reaction indicator, are the most frequently used in driving distraction. Driving performance is the most commonly used single indicator. Support vector machine model is the most commonly used driving distraction detection model, while the standard deviation of detection accuracy is large, and this model is also unstable. In contrast, the detection accuracy of a deep learning algorithm-based model is high, and its stability is good. Future research on driving distraction should balance research topics, expand distraction scenarios to human-machine co-driving, further investigate the types of driving distractions, construct a standardized indicator system and selection principles, and strengthen model construction to detect different types and determine the severity of driving distractions. 11 tabs, 1 fig, 96 refs.

     

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