Driver Movement Patterns Indicate Distraction and Engagement

ObjectiveThis research considers how driver movements in video clips of naturalistic driving are related to observer subjective ratings of distraction and engagement behaviors.BackgroundNaturalistic driving video provides a unique window into driver behavior unmatched by crash data, roadside observations, or driving simulator experiments. However, manually coding many thousands of hours of video is impractical. An objective method is needed to identify driver behaviors suggestive of distracted or disengaged driving for automated computer vision analysis to access this rich source of data.MethodVisual analog scales ranging from 0 to 10 were created, and observers rated their perception of driver distraction and engagement behaviors from selected naturalistic driving videos. Driver kinematics time series were extracted from frame-by-frame coding of driver motions, including head rotation, head flexion/extension, and hands on/off the steering wheel.ResultsThe ratings were consistent among participants. A statistical model predicting average ratings from the kinematic features accounted for 54% of distraction rating variance and 50% of engagement rating variance.ConclusionRated distraction behavior was positively related to the magnitude of head rotation and fraction of time the hands were off the wheel. Rated engagement behavior was positively related to the variation of head rotation and negatively related to the fraction of time the hands were off the wheel.ApplicationIf automated computer vision can code simple kinematic features, such as driver head and hand movements, then large-volume naturalistic driving videos could be automatically analyzed to identify instances when drivers were distracted or disengaged.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 844-860
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01642085
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 27 2017 10:05AM