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Eye-Tracking-Based Evaluation of Cognitive Style and Driving Task Effects on AR-HUD Navigation Interfaces.

Jing Li^1,2, Xinyu Feng¹, Min Lin¹

¹College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

Augmented reality head-up displays (AR-HUDs) impact driver performance. Interface design and task type influence reaction times and visual attention, with specific designs benefiting different driving scenarios.

Keywords:

AR-HUD behavioral performance cognitive style driving task eye tracking navigation design

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Published on: September 18, 2012

Area of Science:

Human-Computer Interaction
Automotive Engineering
Cognitive Psychology

Background:

Augmented reality head-up displays (AR-HUDs) are increasingly common in intelligent vehicles.
Poor AR-HUD design can increase driver cognitive load and response times to hazards.

Purpose of the Study:

To investigate the combined effects of cognitive style, driving task type, and AR-HUD design on driver performance and visual attention.
To provide evidence for dynamic AR-HUD interface optimization.

Main Methods:

A driving simulation experiment with 38 participants comparing world-fixed (WF) and screen-fixed (SF) AR-HUD interfaces.
Tested across goal-directed and stimulus-driven tasks, analyzing reaction times and eye-tracking data.
Participants were screened for cognitive style using the Group Embedded Figures Test.

Main Results:

Stimulus-driven tasks, especially rear-vehicle scenarios, significantly increased reaction times.
WF displays reduced fixation duration and counts during lane-change tasks.
SF displays enhanced attentional efficiency in pedestrian-warning tasks; field-dependent drivers showed higher cognitive workload.

Conclusions:

AR-HUD interface design significantly impacts driver behavioral performance and visual attention.
Dynamic optimization of AR-HUDs based on task context and cognitive workload is recommended.
Findings support sensor-based adaptive interfaces for intelligent vehicles.