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Research Paper | Information Technology | India | Volume 11 Issue 7, July 2022

Driver Drowsiness Detection System Using Artificial Intelligence

Chetan Birari

Abstract: An increased vehicle on roads and the application of lack of traffic rules leads to many human error crashes and leads to deaths of people. In this paper, we recommend the driver monitor and assist a device that uses IoT sensors, such as alcohol sensor and the ultrasonic sensor to assess mental alertness as well machine learning algorithms to get little and regular sleep yams to get drowsiness. It also monitors the Eye Aspect Ratio (EAR) as well the driver's Mouth Aspect Ratio (MAR) arrives at the set number of frames to check sleep and yawning. As a result, the system is extremely sensitive to the detection of sleep. This also necessitated the implementation of a facial recognition function, as most drivers monitoring is done individually. The device constantly monitors the driver using the camera for indicators of sleepiness, and utilises a buzzer to warn a drowsy driver, according to our test results. The purpose of our work is to improve as well prevent drunken driving and drowsiness driving behavior in drivers. This IOT based application help to measure the real time drowsiness of driver using the sensors and give the best results which can help to predict the false drowsiness.

Keywords: Eye Aspect Ratio, Mouth Aspect Ratio, Yawn Detection, Facial Landmarks, IOT Sensors, Machine Learning, Fatigue Check, Drowsiness Detection

Edition: Volume 11 Issue 7, July 2022,

Pages: 1353 - 1358