Modelling and Detection of Driver's Fatigue using Ontology

TL;DR

This paper proposes an ontology-based system to model and detect driver fatigue by analyzing vehicle and physiological data, aiming to enhance road safety through early warning alerts.

Contribution

It introduces an ontological framework integrating vehicle and driver data for real-time fatigue detection, which is a novel approach in driver safety systems.

Findings

Effective detection of driver fatigue using ontological rules

Integration of vehicle and physiological data improves accuracy

Potential to reduce fatigue-related accidents

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.