Internet of Things Assisted Artificial Intelligence Enabled Drowsiness Detection Framework

Drowsiness, drunk driving, and fatigue are major causes of car accidents and consequent deaths nowadays. In this research, we introduced a real-time, Internet-of-Things (IoT) assisted, Computer Vision (CV) enabled framework for monitoring driver sleepiness based on Eye Aspect Ratio (EAR). When the EAR ratio drops below 0.2, a warning error is generated using a text-to-voice converter. The driver is alerted via seat belt vibrators; this data is sent to a cloud server. If the driver continues to sleep or fatigues for more than 2 seconds after the warning mechanism, the acceleration of the car might be lowered to prevent the accident. With the help of the NoIR Camera and Machine Learning (ML) model, images can now see well both during the day and at night without compromising the image quality. The efficacy of the developed model is confirmed by testing in multiple test scenarios using different subjects on a Raspberry Pi 4B Graphics Processing Unit (GPU) computer. As a result, the suggested technique has detected driver exhaustion and raised awareness, and prevented the accident rate.