Discriminating between multiple driving events using physiological signals remains challenging. This study examined whether gaze dynamics and pupil responses could distinguish multiple driving events and reveal age-related physiological patterns. Twenty-seven participants (12 young, 15 old) completed a simulated driving scenario featuring nine events while physiological signals were recorded. Statistical analyses revealed significant event-specific modulation of gaze dispersion metrics ( = 0.571–0.582, ). Gradient boosted trees achieved 82.4% accuracy (95% CI [74.3%, 89.8%]) classifying four events (Bicycle, DeerAlert, LowGas, StopAtGasStation) using 7 gaze-based features, with SHAP analysis identifying horizontal deviation standard deviation and median as primary discriminators. Time-series motif discovery revealed consistent gaze motif amplitudes (3.0–3.4 z-scores) across events, while pupil motifs showed selective enhancement during monitoring tasks (3.1–3.8 z-scores). Age-stratified analyses uncovered distinct physiological patterns: younger drivers exhibited greater gaze variability with broader scanning patterns, whereas older drivers demonstrated elevated pupil motif amplitudes (particularly during LowGas: 3.88 vs. 3.18 z-scores) alongside more constrained visual exploration. The convergence of statistical, machine learning, and motif discovery approaches establishes gaze dynamics as sufficient for multiclass event discrimination in simulated driving. These findings demonstrate that gaze-based features alone can reliably distinguish between specific driving events, while motif analysis reveals temporal dynamics and age-related patterns that warrant further investigation in larger samples, providing a foundation for interpretable driver monitoring systems.

Accepted version

This study aimed to identify which information should be displayed on a head-up display (HUD) in semi-automated vehicles to enable the driver to maintain better situational awareness during the manual operation of the vehicle. It further explored how does the size of the field of view of the HUD affects such information presentation, while taking into consideration user's personal preferences and opinions. Four head-up display interfaces were developed differing in the amount, frequency and field of view of visual information presentation, and were implemented in a simulated semi-automated vehicle. The HUDs were evaluated in a user-study with a within-subject design with 30 participants. The obtained results revealed that versions with smaller and larger field of view HUDsevoke similar driving performance, levels of cognitive load, user experience and perceived usability, suggesting that the HUD's field of view size does not have an overall significant effect on the driver's situational awareness. The results reveal that display of information, which can help with obtaining and maintaining higher situational awareness levels, contribute to better driving performance. Drivers on the other hand, prefer also display of information for lower situational awareness levels.