Driver Drowsiness Recognition System Using Raspberry Pi
Year: 2025
Type: article
Abstract: This paper introduces a Driver Drowsiness Recognition System to enhance road safety by continuously monitoring the alertness of drivers. The system uses the Raspberry Pi 4B as the main controller and combines image processing with fatigue detection algorithms. A switch-based system monitors seatbelt use, while a camera conducts continuous monitoring prior to and during driving. The system has several levels of safety built into it, such as detection of alcohol consumption, seatbelt checks, and real-time observation of drowsiness and yawning through computer vision methods. Facial features are examined to identify fatigue signs, and a Convolutional Neural Network (CNN) model scans eye and face conditions to detect drowsiness. When fatigue indicators are sensed, warnings are presented by a buzzer and vibration motor, and repeated warnings will ultimately cause an imitation vehicle shut down. The system proposed herein provides a intelligent, responsive, and efficient mechanism for preventing fatigue-induced accidents.
Authors: Amish Kukudkar, Prathamesh Kale, Sushil Kanje, Dr. M.B.Mali
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893312830
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