• DocumentCode
    1147725
  • Title

    On the Roles of Eye Gaze and Head Dynamics in Predicting Driver´s Intent to Change Lanes

  • Author

    Doshi, Anup ; Trivedi, Mohan Manubhai

  • Author_Institution
    Lab. for Safe & Intell. Automobiles (LISA), Univ. of California, La Jolla, CA, USA
  • Volume
    10
  • Issue
    3
  • fYear
    2009
  • Firstpage
    453
  • Lastpage
    462
  • Abstract
    Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p < 0.01) 3 s ahead of lane-change situations, indicating that there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.
  • Keywords
    driver information systems; driver behavioral cues; eye gaze; head dynamics; intelligent advanced driver-assistance systems; lane position; lane-change intent-prediction system; vehicle dynamics; Driver-assistance systems; driver behavior; driver intent inference; intelligent vehicles; machine vision; sparse Bayesian learning;
  • fLanguage
    English
  • Journal_Title
    Intelligent Transportation Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1524-9050
  • Type

    jour

  • DOI
    10.1109/TITS.2009.2026675
  • Filename
    5173535