2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Doi
Abstract
—This paper presents a multimodal approach to invehicle
classification of driver glances. Driver glance is a
strong predictor of cognitive load and is a useful input to
many applications in the automotive domain. Six descriptive
glance regions are defined and a classifier is trained on video
recordings of drivers from a single low-cost camera. Visual
features such as head orientation, eye gaze and confidence
ratings are extracted, then statistical methods are used to
perform failure analysis and calibration on the visual features.
Non-visual features such as steering wheel angle and indicator
position are extracted from a RaceLogic VBOX system. The
approach is evaluated on a dataset containing multiple 60
second samples from 14 participants recorded while driving in
a natural environment. We compare our multimodal approach
to separate unimodal approaches using both Support Vector
Machine (SVM) and Random Forests (RF) classifiers. RF
Mean Decrease in Gini Index is used to rank selected features
which gives insight into the selected features and improves the
classifier performance. We demonstrate that our multimodal
approach yields significantly higher results than unimodal
approaches. The final model achieves an average F1 score of
70.5% across the six classes