Assessing Distraction Potential of Augmented Reality Head-Up Displays for Vehicle Drivers. (August 2022)

• Record Type: Journal Article
• Authors: Kim, Hyungil; Gabbard, Joseph L.
• Is Part Of: Human factors. Volume 64:Number 5(2022)
• Page Start: 852
• Page End: 865
• Publication Date: 2022-08
• Subjects: augmented reality -- interface evaluation -- situation awareness -- driver distraction; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720819844845
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: To develop a framework for quantifying the visual and cognitive distraction potential of augmented reality (AR) head-up displays (HUDs). Background: AR HUDs promise to be less distractive than traditional in-vehicle displays because they project information onto the driver's forward-looking view of the road. However, AR graphics may direct the driver's attention away from critical road elements. Moreover, current in-vehicle device assessment methods, which are based on eyes-off-road time measures, cannot capture this unique challenge. Method: This article proposes a new method for the assessment of AR HUDs by measuring driver gaze behavior, situation awareness, confidence, and workload. An experimental user study ( n = 24) was conducted in a driving simulator to apply the proposed method for the assessment of two AR pedestrian collision warning (PCW) design alternatives. Results: Only one of the two tested AR interfaces improved driver awareness of pedestrians without visually and cognitively distracting drivers from other road elements that were not augmented by the display but still critical for safe driving. Conclusion: Our initial human-subject experiment demonstrated the potential of the proposed method in quantifying both positive and negative consequences of AR HUDs on driver cognitive processes. More importantly, the study suggests that AR interfaces can be informative or distractive depending on the perceptual forms of graphical elements presented on the displays. Application: The proposed methods can be applied by designers of in-vehicle AR HUD interfaces and be leveraged by designers of AR user interfaces in general.