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Vehicles with a safety function for anticipating crashes in advance canenhance drivers' ability to avoid crashes. As dashboard cameras have become alow-cost sensor device accessible to almost every vehicle, deep neural networksfor crash anticipation from a dashboard camera are receiving growing interest.However, drivers' trust in the Artificial Intelligence (AI)-enabled safetyfunction is built on the validation of its safety enhancement toward zerodeaths. This paper is motivated to establish a method that uses gaze data andcorresponding measures to evaluate human drivers' ability to anticipatecrashes. A laboratory experiment is designed and performed, wherein ascreen-based eye tracker collects the gaze data of six volunteers whilewatching 100 driving videos that include both normal and crash scenarios.Statistical analyses of the experimental data show that, on average, driverscan anticipate a crash up to 2.61 seconds before it occurs in this pilot study.The chance that drivers have successfully anticipated crashes before they occuris 92.8%. A state-of-the-art AI model can anticipate crashes 1.02 secondsearlier than drivers on average. The study finds that crash-involving trafficagents in the driving videos can vary drivers' instant attention level, averageattention level, and spatial attention distribution. This finding supports thedevelopment of a spatial-temporal attention mechanism for AI models tostrengthen their ability to anticipate crashes. Results from the comparisonalso suggest the development of collaborative intelligence that keepshuman-in-the-loop of AI models to further enhance the reliability of AI-enabledsafety functions.

A Gaze Data-based Comparative Study to Build a Trustworthy Human-AI Collaboration in Crash Anticipation