Authors:
Alexandre Lambert
1
;
Manolo Dulva Hina
1
;
Celine Barth
1
;
Assia Soukane
1
and
Amar Ramdane-Cherif
2
Affiliations:
1
Inseec U Research Center, ECE Paris School of Engineering, 37 quai de Grenelle, 75015 Paris, France
;
2
LISV Laboratory, Université de Versailles – Paris Saclay, 10-12 avenue de l’Europe, 78140 Vélizy, France
Keyword(s):
Ontology, Driver Fatigue, Context Modelling, Safe Driving, Perception, Data Fusion.
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.