IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p445-d1016718.html
   My bibliography  Save this article

Studying Driver’s Perception Arousal and Takeover Performance in Autonomous Driving

Author

Listed:
  • Qiuhong Wang

    (Research Institute for Road Safety of MPS, No. 3 Chongwenmenwai Street, Beijing 100062, China)

  • Haolin Chen

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Jianguo Gong

    (Research Institute for Road Safety of MPS, No. 3 Chongwenmenwai Street, Beijing 100062, China
    School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China)

  • Xiaohua Zhao

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Zhenlong Li

    (Beijing Engineering Research Center of Urban Transport Operation Guarantee, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

The driver’s perception level and takeover performance are two major factors that result in accidents in autonomous vehicles. This study’s goal is to analyze the change in drivers’ perception level and its influence on takeover performance during autonomous driving. A takeover behavior test platform is implemented based on a high-fidelity driving simulator. The fog zone is selected as the takeover scenario. Thus, a 2 (takeover request time: 5 s, 10 s) by 2 (non-driving-related task: work task, entertainment task) takeover experiment was conducted. A generalized linear mixed model is developed to explore the influence of the perception level on takeover performance. The study finds out that, after the takeover request is triggered, the driver’s gaze duration is shortened and the pupil area is enlarged, which is helpful for the driver to extract and understand the road information faster. Male drivers have greater perception levels than female drivers, and they prioritize leisure tasks more than professional ones. The drivers’ perception level decreases when age increases. The shorter the gaze duration is, and the larger the pupil area is, the shorter the takeover response time will be. In addition, drivers’ perception level has a positive effect on takeover performance. Finally, this study provides a reference for revealing the changing rules of drivers’ perception level in autonomous driving, and the study can provide support for the diagnosis of takeover risks of autonomous vehicles from the perspective of human factors.

Suggested Citation

  • Qiuhong Wang & Haolin Chen & Jianguo Gong & Xiaohua Zhao & Zhenlong Li, 2022. "Studying Driver’s Perception Arousal and Takeover Performance in Autonomous Driving," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:445-:d:1016718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/445/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/445/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nielsen, Thomas Alexander Sick & Haustein, Sonja, 2018. "On sceptics and enthusiasts: What are the expectations towards self-driving cars?," Transport Policy, Elsevier, vol. 66(C), pages 49-55.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yaxi Han & Tao Wang & Dong Shi & Xiaofei Ye & Quan Yuan, 2023. "The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Goletz, Mirko & Haustein, Sonja & Wolking, Christina & L’Hostis, Alain, 2020. "Intermodality in European metropolises: The current state of the art, and the results of an expert survey covering Berlin, Copenhagen, Hamburg and Paris," Transport Policy, Elsevier, vol. 94(C), pages 109-122.
    2. Kassens-Noor, Eva & Kotval-Karamchandani, Zeenat & Cai, Meng, 2020. "Willingness to ride and perceptions of autonomous public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 92-104.
    3. Faber, Koen & van Lierop, Dea, 2020. "How will older adults use automated vehicles? Assessing the role of AVs in overcoming perceived mobility barriers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 353-363.
    4. Guo, Yuntao & Souders, Dustin & Labi, Samuel & Peeta, Srinivas & Benedyk, Irina & Li, Yujie, 2021. "Paving the way for autonomous Vehicles: Understanding autonomous vehicle adoption and vehicle fuel choice under user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 364-398.
    5. Martina Raue & Lisa A. D'Ambrosio & Carley Ward & Chaiwoo Lee & Claire Jacquillat & Joseph F. Coughlin, 2019. "The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 358-374, February.
    6. Peng Jing & Gang Xu & Yuexia Chen & Yuji Shi & Fengping Zhan, 2020. "The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review," Sustainability, MDPI, vol. 12(5), pages 1-26, February.
    7. Andreja Pucihar & Iztok Zajc & Radovan Sernec & Gregor Lenart, 2019. "Living Lab as an Ecosystem for Development, Demonstration and Assessment of Autonomous Mobility Solutions," Sustainability, MDPI, vol. 11(15), pages 1-21, July.
    8. Chee, Pei Nen Esther & Susilo, Yusak O. & Wong, Yiik Diew, 2020. "Determinants of intention-to-use first-/last-mile automated bus service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 350-375.
    9. Du, Manqing & Zhang, Tingru & Liu, Jinting & Xu, Zhigang & Liu, Peng, 2022. "Rumors in the air? Exploring public misconceptions about automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 237-252.
    10. Ljubi, Klara & Groznik, Aleš, 2023. "Role played by social factors and privacy concerns in autonomous vehicle adoption," Transport Policy, Elsevier, vol. 132(C), pages 1-15.
    11. Yaxi Han & Tao Wang & Dong Shi & Xiaofei Ye & Quan Yuan, 2023. "The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    12. Yukari Jessica Tham & Takaaki Hashimoto & Kaori Karasawa, 2022. "Underlying dimensions of benefit and risk perception and their effects on people’s acceptance of conditionally/fully automated vehicles," Transportation, Springer, vol. 49(6), pages 1715-1736, December.
    13. Liu, Peng & Ma, Yanjiao & Zuo, Yaqing, 2019. "Self-driving vehicles: Are people willing to trade risks for environmental benefits?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 139-149.
    14. Liu, Peng, 2020. "Positive, negative, ambivalent, or indifferent? Exploring the structure of public attitudes toward self-driving vehicles on public roads," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 27-38.
    15. Yue Ding & Ruimin Li & Xiaokun Wang & Joshua Schmid, 2022. "Heterogeneity of autonomous vehicle adoption behavior due to peer effects and prior-AV knowledge," Transportation, Springer, vol. 49(6), pages 1837-1860, December.
    16. Wang, Song & Li, Zhixia & Wang, Yi & Aaron Wyatt, Daniel, 2022. "How do age and gender influence the acceptance of automated vehicles? – Revealing the hidden mediating effects from the built environment and personal factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 376-394.
    17. Zohreh Asadi-Shekari & Ismaïl Saadi & Mario Cools, 2022. "Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    18. Perrine, Kenneth A. & Kockelman, Kara M. & Huang, Yantao, 2020. "Anticipating long-distance travel shifts due to self-driving vehicles," Journal of Transport Geography, Elsevier, vol. 82(C).
    19. Liu, Peng & Xu, Zhigang & Zhao, Xiangmo, 2019. "Road tests of self-driving vehicles: Affective and cognitive pathways in acceptance formation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 354-369.
    20. Ryosuke Abe & Yusuke Kita & Daisuke Fukuda, 2020. "An Experimental Approach to Understanding the Impacts of Monitoring Methods on Use Intentions for Autonomous Vehicle Services: Survey Evidence from Japan," Sustainability, MDPI, vol. 12(6), pages 1-16, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:445-:d:1016718. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.