Toward context-adaptive risk prompting for intelligent vehicles: Integrating the Urgency Index and physiological mechanisms

With the rise of intelligent driving and human–machine codriving, effective yet minimally intrusive risk prompts have become crucial for road safety. While important groundwork on prompt effectiveness has been laid in prior work, there is still an opportunity to build on this foundation and further investigate the joint parametric optimization of frequency, duration, and lead time. In extending prior efforts in scene classification, a need has emerged for the development of data-driven, quantitative approaches to urgency quantification that complement the existing methods. We conducted a within-subject driving simulator study with 40 drivers across nine risk scenarios. Using baseline physiological and behavioral data from a no-prompt control, we constructed an objective urgency index (UI) and evaluated how prompt-parameter combinations affected driving, eye movements, and physiology. The P2–D1–L3 setting (two prompts, 1 s duration, 3 s lead time) was Pareto-optimal, as it improved lateral stability while preserving longitudinal control. Lead time was the dominant factor, and it outperformed frequency and duration. Prompting produced the greatest benefits in medium-urgency scenes, taking an inverted U-shaped moderation effect. Mediation analysis indicated that improved lateral control was driven primarily by physiological arousal, which was indexed by pupil dilation. These findings quantify the optimal parameter boundaries and support the fine-grained design of risk-promotion HMIs in intelligent vehicles.