A smart system for driver's fatigue detection, remote notification and semi-automatic parking of vehicles to prevent road accidents

Abstract

Drowsy driving is one of the main reasons of road accidents. Different techniques have been reported in literature to detect driver's drowsiness, but almost all the prevailing systems only alert the driver if drowsiness is detected. Consequently, the drowsy driver continues driving, with a high risk of devastating accident. In this paper, we proposed and verified an EEG based system which not only alerts the driver by alarm, but also puts the vehicle in semiautomatic parking mode by controlling fuel supply if drowsiness is detected. At the same time, it reports nearby police station by SMS which contains necessary information to take essential steps locating the vehicle. Stored EEG signals, obtained with wireless wearable headsets from numerous subjects in different conditions by different research groups, were used in this work. Power spectrum analyses were carried out in MATLAB to determine the dominant frequency components in the brain signals. The slow wave to fast wave ratios of EEG activities were assessed for a number of epochs to determine driver's drowsiness. GPS and GSM modules were used with Arduino MEGA for tracking, remote notification and servomotor control. Performance of the proposed system was evaluated by stored data which confirmed its feasibility and reliability.