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Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China

Author

Listed:
  • Nengchao Lyu

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
    Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China)

  • Lian Xie

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
    Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China
    School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China)

  • Chaozhong Wu

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
    Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China)

  • Qiang Fu

    (Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, China)

  • Chao Deng

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
    Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China)

Abstract

Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs. Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance.

Suggested Citation

  • Nengchao Lyu & Lian Xie & Chaozhong Wu & Qiang Fu & Chao Deng, 2017. "Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China," IJERPH, MDPI, vol. 14(2), pages 1-25, February.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:2:p:203-:d:90732
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    Citations

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    Cited by:

    1. Chen Xu & Decun Dong & Dongxiu Ou & Changxi Ma, 2019. "Time-of-Day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections," IJERPH, MDPI, vol. 16(5), pages 1-18, March.
    2. Lei Han & Zhigang Du & Shoushuo Wang & Ying Chen, 2022. "Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    3. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Anwar Ameen Hezam Saeed & Ahmed Farouk Kineber, 2021. "Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    4. Lian Xie & Jiaxin Zhang & Rui Cheng, 2023. "Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    5. Changxi Ma & Dong Yang & Jibiao Zhou & Zhongxiang Feng & Quan Yuan, 2019. "Risk Riding Behaviors of Urban E-Bikes: A Literature Review," IJERPH, MDPI, vol. 16(13), pages 1-18, June.

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