Identification of Real-Time Diagnostic Measures of Visual Distraction With an Automatic Eye-Tracking System

Harry Zhang · Gerald J. Witt

82 citations0/0 files storedHuman Factors The Journal of the Human Factors and Ergonomics Society

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Human-Automation Interaction and Safety Traffic and Road Safety Sleep and Work-Related Fatigue

Abstract

OBJECTIVE: This study was conducted to identify eye glance measures that are diagnostic of visual distraction. BACKGROUND: Visual distraction degrades performance, but real-time diagnostic measures have not been identified. METHOD: In a driving simulator, 14 participants responded to a lead vehicle braking at -2 or -2.7 m/s2 periodically while reading a varying number of words (6-15 words every 13 s) on peripheral displays (with diagonal eccentricities of 24 degrees, 43 degrees, and 75 degrees). RESULTS: As the number of words and display eccentricity increased, total glance duration and reaction time increased and driving performance suffered. CONCLUSION: Correlation coefficients between several glance measures and reaction time or performance variables were reliably high, indicating that these glance measures are diagnostic of visual distraction. It is predicted that for every 25% increase in total glance duration, reaction time is increased by 0.39 s and standard deviation of lane position is increased by 0.06 m. APPLICATION: Potential applications of this research include assessing visual distraction in real time, delivering advisories to distracted drivers to reorient their attention to driving, and using distraction information to adapt forward collision and lane departure warning systems to enhance system effectiveness.

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References

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