Explicit and implicit HMI for tunnel blind spot: insights from a naturalistic driving experiment

OBJECTIVES: Cooperative Vehicle-Infrastructure Systems (CVIS) can significantly enhance tunnel safety, yet most research relies on driving simulators that lack the psychological realism of actual confined environments. This study addresses the gap by evaluating Human-Machine Interaction (HMI) effectiveness in real-world tunnel blind spots, where restricted visibility and confined roadway geometry create high-risk scenarios. METHODS: A naturalistic driving experiment was conducted in the Guayanling Tunnel, China. Participants drove a vehicle equipped with eye-tracking and onboard data units through a curved blind spot under four conditions: no warning, informative (implicit), instructive, and commanding (explicit) warnings. The study analyzed vehicle data (speed, braking, lateral acceleration) and eye-tracking metrics to assess safety and effectiveness, while also examining the influence of driver gender, experience, and driving style. RESULTS: Across the fixed sequential warning rounds, commanding warnings were associated with the largest changes in speed regulation and earlier braking, but they were also associated with increased lateral-control variability. Instructive warnings also advanced braking, whereas informative warnings showed smaller behavioral effects. Formal interaction analyses provided limited support for subgroup moderation: braking responses varied by driving experience, whereas gender- and driving-style-related differences were weak or exploratory. CONCLUSIONS: Explicit, commanding HMIs are superior for immediate hazard response in tunnel blind spots but impose a higher cognitive load. The effectiveness of warnings is not universal; it varies significantly based on individual driver characteristics. Therefore, relying solely on generic warning strategies may be insufficient for diverse driver populations.