title: Identification of real-time diagnostic measures of visual distraction with an automatic eye-tracking system.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Delphi Electronics & Safety, Kokomo, Indiana, USA. harryzhang@motorola.com
authors.forename: Harry
authors.initials: H
authors.lastname: Zhang
authors.forename: Matthew R H
authors.initials: MR
authors.lastname: Smith
authors.forename: Gerald J
authors.initials: GJ
authors.lastname: Witt
mesh_terms: D000328:Adult; D001288:Attention; D001334:Automobile Driving; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011930:Reaction Time; D012106:Research; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D014796:Visual Perception