Abstract
Road accidents caused by driver fatigue and pedestrian collisions are a major concern worldwide. Drivers often become drowsy during long journeys, which reduces their concentration and reaction time, increasing the risk of accidents. Pedestrians are also among the most vulnerable road users and are often involved in road accidents due to delayed driver response. The proposed system, DROWZISHIELD, is an AI-based safety system that detects driver drowsiness and pedestrians in real time using computer vision techniques. The system monitors the driver's facial features to detect fatigue and simultaneously analyzes the road environment to identify pedestrians using object detection models. When the system detects a risky situation, it generates alerts to warn the driver. This approach enhances road safety by improving driver awareness and preventing potential accidents.
Keywords
Artificial intelligence, computer vision, driver drowsiness detection, pedestrian detection, road safety, YOLO, OpenCV
Citations
IRE Journals:
Aditya Kharade, Sarthak Shinde, Sarvesh Wani, Kalpana Gangwar "DROWZISHIELD: An AI-Based Real-Time Driver Drowsiness and Pedestrian Detection System" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2178-2181 https://doi.org/10.64388/IREV9I10-1716602
IEEE:
Aditya Kharade, Sarthak Shinde, Sarvesh Wani, Kalpana Gangwar
"DROWZISHIELD: An AI-Based Real-Time Driver Drowsiness and Pedestrian Detection System" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716602
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