Smart Baggage Tracker

Abstract

A concerning rise in accidents caused by human error has resulted from the proliferation of vehicles on Indian roads and the inadequate enforcement of traffic regulations. This project proposes a sophisticated Driver Safety System that uses machine learning (ML) algorithms and Internet of Things (IoT) sensors to address two primary causes of traffic accidents: tiredness and driving under the influence (DUI).Our solution uses machine learning (ML) algorithms to identify minute indicators of driver drowsiness, like frequent yawning and microsleep, and uses an Internet of Things device with air pressure and alcohol sensors for real-time sobriety checks. By asking the motorist to blow into a mouthpiece to start a sobriety check, the device only permits ignition after a clean and appropriate blow. It then uses a camera to continuously monitor the driver, alerting sleepy drivers with either a buzzer or the sound system of the car.Our proactive method reduces the likelihood of impaired driving-related accidents by offering real-time DUI offence warnings, in contrast to conventional sobriety checkpoints and current IoT-based solutions. Additionally, by spotting fatigue indicators, the ML-based sleepiness detection system increases safety and helps lower the overall number of accidents caused by human error.In addition to encouraging a culture of responsible driving and improving safety, this project seeks to provide an efficient and cutting-edge technical solution to lessen the negative effects of driving events involving intoxication and sleepiness.