@article{ref1,
title="Methodology and mobile application for driver behavior analysis and accident prevention",
journal="IEEE transactions on intelligent transportation systems",
year="2020",
author="Kashevnik, Alexey and Lashkov, Igor and Gurtov, Andrei",
volume="21",
number="6",
pages="2427-2436",
abstract="This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal smartphone. For the driver behavior monitoring, the smartphone's cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following driver's online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.<p /> <p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2019.2918328",
url="http://dx.doi.org/10.1109/TITS.2019.2918328"
}