IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i1p483-d716750.html
   My bibliography  Save this article

Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China

Author

Listed:
  • Jianguo Gong

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

  • Xiucheng Guo

    (School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China)

  • Lingfeng Pan

    (School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China)

  • Cong Qi

    (School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China)

  • Ying Wang

    (Department of Road Traffic Management, Beijing Police College, Beijing 102202, China)

Abstract

Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship between age and takeover behavior in automated driving, where 16 test conditions were taken into consideration, including two driving tasks, two warning times and four driving scenarios. Forty-two drivers in Beijing, China in 2020 were recruited to participate in a static driving simulator with Level 3 (L3) conditional automation to obtain detailed test information of the recorded takeover time, mean speed and mean lateral offset. An ANOVA test was proposed to examine the significance among different age groups and conditions. The results confirmed that reaction time increased significantly with age and the driving stability of the older group was worse than the young and middle groups. It was also indicated that the older group could not adapt to complex tasks well when driving due to their limited cognitive driving ability. Additionally, the higher urgency of a scenario explained the variance in the takeover quality. According to the obtained influencing mechanisms, policy implications for the development of vehicle automation, considering the various driving behaviors of drivers, were put forward, so as to correctly identify the high-risk driving conditions in different age groups. For further research, on-road validation will be necessary in order to check for driving simulation-related effects.

Suggested Citation

  • Jianguo Gong & Xiucheng Guo & Lingfeng Pan & Cong Qi & Ying Wang, 2022. "Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(1), pages 1-11, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:1:p:483-:d:716750
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/1/483/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/1/483/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jianguo Gong & Xiucheng Guo & Cong Qi & Lingfeng Pan & Xiaochen Liu, 2023. "Research on Assessing Driving Ability of Older Drivers Based on Cognitive Tests: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(4), pages 1-10, February.

    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:14:y:2022:i:1:p:483-:d:716750. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.