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

As augmented reality head-up display (AR-HUD) becomes increasingly integrated into intelligent vehicles, inappropriate interface designs may increase drivers’ cognitive workload and delay hazard responses. This study investigates how cognitive style, driving task type, and AR-HUD navigation design jointly influence drivers’ behavioral performance and visual attention. A total of 55 participants were recruited and screened using the Group Embedded Figures Test, with 38 drivers finally selected for a 2 × 4 × 2 driving-simulation experiment comparing world-fixed (WF) and screen-fixed (SF) interfaces across goal-directed and stimulus-driven tasks. Reaction times and eye-tracking indicators were analyzed using generalized linear models. Results show that stimulus-driven tasks significantly increased reaction times, with rear-vehicle scenarios producing the longest responses (mean = 1.420). During lane-change tasks, WF displays significantly reduced fixation duration (p < 0.001) and fixation counts (p < 0.001), whereas SF displays improved attentional efficiency during pedestrian-warning tasks. In addition, field-dependent drivers exhibited significantly larger pupil diameters, indicating higher cognitive workload. These findings provide sensor-based evidence for AR-HUD systems that dynamically optimize interface presentation according to task context and workload conditions.