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

Zhendong Wu, Ying Liang, Guocui Liu and Xiaoqun Ai ()
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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

Sustainability, 2024, vol. 16, issue 21, 1-20

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.

Keywords: AR-HUDs; crash warning icon systems; driving safety; eye-tracking; visual attention (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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