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The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Author

Listed:
  • Linhong Wang

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

  • Hongtao Li

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

  • Mengzhu Guo

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

  • Yixin Chen

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

Abstract

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1881-:d:744207
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    Citations

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    Cited by:

    1. Mariusz Kostrzewski & Magdalena Marczewska & Lorna Uden, 2023. "The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance," Energies, MDPI, vol. 16(7), pages 1-20, April.
    2. Honggang Li & Jiankai Li & Hongtao Li & Jiangwei Chu & Qiqi Miao, 2024. "The Sustainability and Energy Efficiency of Connected and Automated Vehicles from the Perspective of Public Acceptance towards Platoon Control," Sustainability, MDPI, vol. 16(2), pages 1-14, January.
    3. Wenlong Liu & Hongtao Li & Hui Zhang, 2022. "Dangerous Driving Behavior Recognition Based on Hand Trajectory," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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