Smart IoT based Early Stage Drowsy Driver Detection Management System

2022 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON) Pub Date : 2022-11-26 DOI:10.1109/EDKCON56221.2022.10032950

J. Samadder, J. Das, Diptangshu Das, Rakesh Sadhukhan, A. Parvin

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Abstract

This study aimed to develop an innovative alert management system for creating smart cars that detect drowsy driving and prevent it automatically. However, sleepiness is a common physiological occurrence in humans that can occur for various reasons. To prevent the accident's cause, a reliable management system must be designed. In this suggested work, it is proposed that a drowsy driver warning system was created utilising a method where the Video Stream Processing (VSP) used an Eye Aspect Ratio (EAR) and Euclidean distance of the eye to study the eye blink concept. The facial landmark method is also used to distinguish eyes with accuracy. The IoT module delivers a warning message with collision incidence and position information, alarms via voice speaking, and notifies nearby traffic/the owner of the car over the Raspberry Pi tracking system when the driver's fatigue is detected. The suggested model excels in that it can identify tiredness in both daytime and nighttime vision with obstacles at different distances with an accuracy greater than 98%.

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基于智能物联网的早期疲劳驾驶检测管理系统

该研究旨在开发一种创新的警报管理系统，以创造能够自动检测并预防疲劳驾驶的智能汽车。然而，困倦是人类常见的生理现象，可能有各种原因。为了防止事故的发生，必须设计可靠的管理系统。在这项建议的工作中，提出利用视频流处理(VSP)使用眼睛宽高比(EAR)和眼睛的欧几里得距离来研究眼睛眨眼概念的方法创建一个昏昏欲睡的驾驶员警告系统。人脸标记法也用于准确识别眼睛。物联网模块提供包含碰撞发生率和位置信息的警告信息，通过语音报警，并在检测到驾驶员疲劳时通过树莓派跟踪系统通知附近的交通/车主。该模型在不同距离障碍物的白天和夜间视觉下均能识别疲劳，准确率大于98%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON)

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