Effects of Presentation Form and Presentation Timing in AR-HUD Takeover Displays on Driver Visual Attention: An Eye-Tracking Study

Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) integrates real-time environmental sensing with visual information delivery, yet how different information presentation strategies influence driver visual attention during takeover remains underexplored. We examined the effects of presentation form (static/dynamic) and presentation timing (concurrent/progressive) using a 2 × 2 within-subject design. In the experiment, twenty-one licensed drivers viewed prerecorded automated driving takeover scenarios. Eye tracking measured mean fixation duration, mean saccade amplitude, time to first fixation, and fixation count, while ratings assessed usability, acceptance, and intention comprehension. Progressive presentation significantly reduced all eye-tracking measures and improved perceived usability ratings compared with concurrent presentation. This pattern indicates more concentrated and orderly gaze allocation and is consistent with lower visual-search and information-integration demands. However, the lower fixation count may partly reflect reduced information exposure. Static presentation reduced mean fixation duration but showed no consistent overall advantage on the remaining measures. Significant form-by-timing interactions showed that progressive presentation reduced saccade amplitude and time to first fixation and improved intention comprehension under static, but not dynamic, presentation. Among the four combinations, static-progressive presentation yielded the most favorable overall pattern. Overall, presentation timing affected more measured outcomes than presentation form. These findings provide preliminary evidence that presentation form and presentation timing shape gaze behavior and subjective evaluations of AR-HUD takeover displays.