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
Link To Document :
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