SURVEY ON REAL TIME DRIVER DROWSINESS DETECTION APP USING ML

Vaibhav Mandavkar; Prof. P. S. Hanwate; Prasad Ligade; Shreyas Kulkarni; Chaitanya Mane

doi:10.17605/OSF.IO/4NYEV

Authors

Vaibhav Mandavkar Computer Engineering Department NBN Sinhgad School of Engineering, Pune-411041, India
Prof. P. S. Hanwate Computer Engineering Department NBN Sinhgad School of Engineering, Pune-411041, India
Prasad Ligade Computer Engineering Department NBN Sinhgad School of Engineering, Pune-411041, India
Shreyas Kulkarni Computer Engineering Department NBN Sinhgad School of Engineering, Pune-411041, India
Chaitanya Mane Computer Engineering Department NBN Sinhgad School of Engineering, Pune-411041, India

DOI:

https://doi.org/10.17605/OSF.IO/4NYEV

Keywords:

Driver Drowsiness, classification, Machine learning, feature extraction, app development, flutter, tensorflow lite.

Abstract

Every year road accidents are increasing rapidly as technological and mechanical advancements in vehicles permits drivers to drive at high speed. Approximately 1.35 million people die each year as a result of road accidents in India alone 151 thousand casualties were recorded last year. From this nearly 78% road accidents are caused due to driver's fault. Main factors for this accidents are drowsiness, drunk and drive and over speeding from which nearly 40% of the accidents caused due to drowsiness. People are conscious about the risk of drinking and driving but don’t realize the dangerous of drowsiness because no instruments exist to measure the driver drowsiness. If the Driver fails to concentrate on driving it reduces the driver reaction time and impairs steering behavior To solve this problem we are going to use the power of machine learning to identify if the driver is drowsy or not. Generally when someone feels drowsy his\hers eye blinking speed decreases by specifying threshold value we can detect if the driver is drowsy or not.