@inproceedings{dua2019autorate,
author = {Dua, Isha and Nambi, Akshay and Jawahar, C. V. and Padmanabhan, Venkat},
title = {AutoRate: How attentive is the driver?},
booktitle = {The 14th IEEE International Conference on Automatic Face and Gesture Recognition},
year = {2019},
month = {May},
abstract = {Driver inattention is one of the leading causes of vehicle crashes and incidents worldwide. Driver inattention includes driver fatigue leading to drowsiness and driver distraction, say due to use of cellphone or rubbernecking, all of which leads to a lack of situational awareness. Hitherto, techniques presented to monitor driver attention evaluated factors such as fatigue and distraction independently. However, in order to develop a robust driver attention monitoring system all the factors affecting driver’s attention needs to be analyzed holistically. In this paper, we present AutoRate, a system that leverages front camera of a windshield-mounted smartphone to monitor driver’s attention by combining several features. We derive a driver attention rating by fusing spatio-temporal features based on the driver state and behavior such as head pose, eye gaze, eye closure, yawns, use of cellphones, etc.

We perform extensive evaluation of AutoRate on realworld driving data and also data from controlled, static vehicle settingswith30driversinalargecity.Wecompare AutoRate’s automatically-generated rating with the scores given by 5 human annotators. Further, we compute the agreement between AutoRate’s rating and human annotator rating using kappa coefficient. AutoRate’s automatically-generated rating has an overall agreement of 0.87 with the ratings provided by 5 human annotators on the static dataset.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/autorate-how-attentive-is-the-driver/},
}