Modelling and Detection of Driver’s Fatigue using Ontology

Alexandre Lambert, Manolo Dulva Hina, Celine Barth, Assia Soukane, Amar Ramdane-Cherif

2021

Abstract

Road accidents have become the eight leading cause of death all over the world. Lots of these accidents are due to a driver’s inattention or lack of focus, due to fatigue. Various factors cause driver’s fatigue. This paper considers all the measureable data that manifest driver’s fatigue, namely those manifested in the vehicle measureable data while driving as well as the driver’s physical and physiological data. Each of the three main factors are further subdivided into smaller details. For example, the vehicle’s data is composed of the values obtained from the steering wheel’s angle, yaw angle, the position on the lane, and the speed and acceleration of the vehicle while moving. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system so that on the first sign of dangerous level of fatigue is detected, a warning notification is sent to the driver. This work is intended to contribute to safe road driving.

Paper Citation

in Harvard Style

Lambert A., Hina M., Barth C., Soukane A. and Ramdane-Cherif A. (2021). Modelling and Detection of Driver’s Fatigue using Ontology. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD; ISBN 978-989-758-533-3, SciTePress, pages 84-95. DOI: 10.5220/0010689700003064

in Bibtex Style

@conference{keod21,
author={Alexandre Lambert and Manolo Dulva Hina and Celine Barth and Assia Soukane and Amar Ramdane-Cherif},
title={Modelling and Detection of Driver’s Fatigue using Ontology},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD},
year={2021},
pages={84-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010689700003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD
TI - Modelling and Detection of Driver’s Fatigue using Ontology
SN - 978-989-758-533-3
AU - Lambert A.
AU - Hina M.
AU - Barth C.
AU - Soukane A.
AU - Ramdane-Cherif A.
PY - 2021
SP - 84
EP - 95
DO - 10.5220/0010689700003064
PB - SciTePress