IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i21p9167-d1504192.html
   My bibliography  Save this article

Comparative Analysis of AR-HUDs Crash Warning Icon Designs: An Eye-Tracking Study Using 360° Panoramic Driving Simulation

Author

Listed:
  • Zhendong Wu

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China)

  • Ying Liang

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China)

  • Guocui Liu

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China)

  • Xiaoqun Ai

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China)

Abstract

Augmented Reality Head-Up Displays (AR-HUDs) enhance driver perception and safety, yet optimal hazard warning design remains unclear. This study examines three AR-HUD crash warning icon types (BD, BR, BW) across various turning scenarios. Using a 360-degree video-based driving simulation with 36 participants, eye-tracking metrics were collected. Results show BW icons, dynamically linked to hazards, significantly improve drivers’ pedestrian risk awareness and visual attention allocation compared to BD and BR systems. BW consistently demonstrated longer gaze duration, higher fixation counts, and shorter time to first fixation across all turns. BD and BR icons were more susceptible to lane changes, potentially diverting attention from hazards. Findings suggest prioritizing dynamic tracking warning icons over fixed-position alternatives to minimize visual competition and distraction, providing crucial insights for AR-HUD optimization in automated vehicles.

Suggested Citation

  • Zhendong Wu & Ying Liang & Guocui Liu & Xiaoqun Ai, 2024. "Comparative Analysis of AR-HUDs Crash Warning Icon Designs: An Eye-Tracking Study Using 360° Panoramic Driving Simulation," Sustainability, MDPI, vol. 16(21), pages 1-20, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9167-:d:1504192
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/21/9167/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/21/9167/
    Download Restriction: no
    ---><---

    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:16:y:2024:i:21:p:9167-:d:1504192. 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.