A Comparative Study of Classification Models for Predicting Monotonous Driver Drowsiness

Abstract

Early Drowsiness is the main cause for the majority fatigue accidents directly connected to vehicle crashes. This may lead to severe vehicle accidents for the on-road drivers. A major vehicle accident happens based on a microsleep collision by sensing and alerting system. Road accidents occur due to multiple reasons and the fatigue of the driver is amongst the predominant factors. The analysis identified a wide range of models capable of predicting road accident effective interventions A device for detecting the severity of the crash prior to an accident and the parameters obtained by sensors from the pre-crash vehicle. It must be anticipated and averted based on the extent of the upcoming collision. Machine Learning could identify the reality of significance of a driver’s state of mind and predict the collision. The alert would show the severity of the drowsiness and to know the state of the driver by automatic notifications. These lives could have been spared if clinical offices are given at the opportune time.