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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
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