DROWZISHIELD: An AI-Based Real-Time Driver Drowsiness and Pedestrian Detection System

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.

Authors

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-04-21
DOI
https://doi.org/10.64388/irev9i10-1716602
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
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article

DROWZISHIELD: An AI-Based Real-Time Driver Drowsiness and Pedestrian Detection System

Kalpana Gangwar, Sarvesh Wani, Sarthak Shinde, Aditya Kharade
Iconic Research and Engineering Journals
Sleep and Work-Related Fatigue
article

DROWZISHIELD: An AI-Based Real-Time Driver Drowsiness and Pedestrian Detection System

Kalpana Gangwar, Sarvesh Wani, Sarthak Shinde, Aditya Kharade
article en

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.

Iconic Research and Engineering JournalsVol. 9(10)
Sustainable cities and communities
Openalex Percentile: Top 5%
Sleep and Work-Related Fatigue
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