Analysis of Drivers' Head and Eye Movement Correspondence: Predicting Drivers' Glance Location Using Head Rotation Data
- Mauricio Muñoz (Massachusetts Institute of Technology (AgeLab), Cambridge, MA)
- Joonbum Lee (Massachusetts Institute of Technology (AgeLab), Cambridge, MA)
- Bryan Reimer (Massachusetts Institute of Technology (AgeLab), Cambridge, MA)
- Bruce Mehler (Massachusetts Institute of Technology (AgeLab), Cambridge, MA)
- Trent Victor (SAFER Vehicle and Traffic Safety Centre at Chalmers, Gothenburg, Sweden)
Abstract
The relationship between a driver’s glance pattern and corresponding head rotation is not clearly defined. Head rotation and eye glance data drawn from a study conducted by the Virginia Tech Transportation Institute in support of methods development for the Strategic Highway Research Program (SHRP 2) naturalistic driving study were assessed. The data were utilized as input to classifiers that predicted glance allocation to the road and the center stack. A predictive accuracy of 83% was achieved with Hidden Markov Models. Results suggest that although there are individual differences in head-eye correspondence while driving, head-rotation data may be a useful predictor of glance location. Future work needs to investigate the correspondence across a wider range of individuals, traffic conditions, secondary tasks, and areas of interest.
How to Cite:
Muñoz, M. & Lee, J. & Reimer, B. & Mehler, B. & Victor, T., (2015) “Analysis of Drivers' Head and Eye Movement Correspondence: Predicting Drivers' Glance Location Using Head Rotation Data”, Driving Assessment Conference 8(2015), 204-210. doi: https://doi.org/10.17077/drivingassessment.1572
Rights: Copyright © 2015 the author(s)
Publisher Notes
- Honda Outstanding Student Paper Award winner
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