• Shivani Pawtekar

Driver drowsiness detection is a key technology that can prevent disastrous car accidents caused by drowsiness of the driver. Determining the sleepiness and alcohol taken by the driver is one of the definite ways of mapping driver fatigue. This project uses an existing prototype of the drowsiness detection system and an alcohol detection system. This mechanism examines the eyes of the driver and triggers an alarm when he/she is drowsy. Driver exhaustion often becomes a direct cause of many traffic accidents. Therefore, there it is essential to implement the systems that will examine and notify a driver of her/him the bad psycho-physiological situation, which could significantly reduce the number of fatigue-related car accidents. One of the technical probabilities to implement driver drowsiness detection systems is to use the vision-based approach. This report exhibits the driver sleepiness and alcohol detection system. This system acquires the face, but mainly focuses on the eyes of the driver and detects the drowsiness. Using machine learning the system detects and examines drowsiness and after detection, if the driver found sleepy the system triggers an alarm. This system senses the alcohol using MQ3 sensor.

Alcohol Identification, Drowsiness Identification, Computer Vision, Machine Learning, IoT.

"Enhancement Through Machine Learning & Iot For Driver Drowsiness and Alcohol Detection System.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.96-103, December 2018, Available :http://www.jetir.org/papers/JETIREB06010.pdf

"Enhancement Through Machine Learning & Iot For Driver Drowsiness and Alcohol Detection System.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp96-103, December 2018, Available at : http://www.jetir.org/papers/JETIREB06010.pdf