Towards Context-based Fatigue Detection System in Vehicular Area Network

Abstract

Driver fatigue is responsible for up to 30% of fatal car accidents. This issue has been addressed by many scholars in order to save thousands of lives and reduce many costs. The goal of this work is to reduce the number of car accidents caused by mental fatigue or drowsiness. In order to achieve this goal, we propose a personalized Bayesian Network (BN) to detect driver’s fatigue. The detection of driver fatigue is enhanced by combining data that reflects the driver’s performance with context-aware information. The parameters of the system are the angular velocity of the steering wheel, the pressure applied to the gas and brake pedals, the grip force on the steering wheel, weather conditions, current traffic, and time of day. The aforementioned parameters of the network are updated on a regular basis, which makes fatigue detection more reliable. Besides, these parameters allow the system to detect a driver’s fatigue through driving performance which is both individualized and context aware. In our experiment, subjects drove a driving simulator game during six sessions, for a total of one hour. After each session, every subject used the Karolinska Sleepiness Scale (KSS) to rate her fatigue’s level. The system was trained on the data collected separately from each user, allowing BN to be personalized for each subject. The proposed system showed an average accuracy of 96%, and ability to overcome the issue of individual differences and uncertainties which are involved in fatigue detection process.