More explicit is not always better: Boundary conditions for action guidance in hazard notifications across traffic...

With the expansion of vehicle automation, hazard notification systems are increasingly important for safety-critical driver-automation coordination. This study examined the effects of traffic complexity, message type, and delivery modality on user perceptions in a Level 3 conditional automation scenario, where participants supervised automated driving and responded to a pedestrian hazard. Forty participants completed 18 simulator trials in a within-subject design. Traffic complexity significantly affected subjective workload, and interaction effects indicated that the influence of modality and message type varied across complexity levels. Compared with a no-alert baseline, notification conditions improved usability and user experience and increased perceived safety in visual-based conditions; however, workload reductions were not consistent across modalities, and explicit action guidance was not uniformly beneficial, particularly under high traffic complexity. As a supplementary exploratory analysis, a Random Forest model with TreeSHAP was used to summarize model-based predictive sensitivity across the tested conditions (with the no-alert condition used as reference coding). TreeSHAP salience highlighted visual-advisory condition indicators as prominent contributors for usability and user experience, while the impact of modality varied across traffic-complexity levels. These findings provide empirical evidence for designing context-adaptive Human-Machine Interfaces (HMI) that calibrate information transparency to environmental demands, mitigating cognitive overload and supporting safety-relevant acceptance outcomes in conditional automation within the tested factor space.