Investigating Drivers' Head and Glance Correspondence

TL;DR

This study investigates how well head rotation can predict driver gaze, showing that head pose can serve as a reliable, cost-effective surrogate for eye gaze in detecting driver distraction and inattention.

Contribution

It demonstrates that head pose data, analyzed with machine learning, can accurately estimate driver gaze regions, especially for high-eccentricity glances, with up to 83% accuracy.

Findings

Head pose can predict gaze with up to 83% accuracy.

Classification accuracy increases with larger gaze shifts.

Head pose estimation is robust across individual differences.

Abstract

The relationship between a driver's glance pattern and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This study explores the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data with manually coded gaze region data using both a statistical analysis approach and a predictive (i.e., machine learning) approach. For the latter, classification accuracy increased as visual angles between two glance locations increased. In other words, the greater the shift in gaze, the higher the accuracy of classification. This is an intuitive but important concept that we make explicit through our analysis. The highest accuracy achieved was 83% using the method of Hidden Markov Models (HMM) for the binary gaze classification problem of (1) the forward roadway versus (2) the center stack. Results suggest that although there are individual differences in head-glance correspondence while driving, classifier models based on head-rotation data may be robust to these differences and therefore can serve as reasonable estimators for glance location. The results suggest that driver head pose can be used as a surrogate for eye gaze in several key conditions including the identification of high-eccentricity glances. Inexpensive driver head pose tracking may be a key element in detection systems developed to mitigate driver distraction and inattention.