AI - Assisted Driver Safety And Monitoring System

Road accidents remain a significant global concern, causing injuries and fatalities primarily due to driver drowsiness, alcohol consumption, and the lack of real-time monitoring systems. This paper presents an AI-Assisted Driver Safety and Monitoring System designed to enhance road safety through intelligent detection and alert mechanisms. The proposed system integrates multiple sensors, including the MQ2 gas sensor for alcohol detection, the ADXL345 accelerometer for motion and tilt analysis, and a vibration sensor for collision detection. An ESP32-CAM module is utilized for real-time video streaming and image-based processing to detect driver drowsiness. The system employs AI-based decision-making to analyze both sensor data and visual inputs, enabling early detection of abnormal conditions. Upon detection, alerts are generated through a buzzer and mobile notifications, and live monitoring is enabled. The proposed system aims to prevent accidents proactively rather than reacting after occurrence, thereby improving overall driver safety and reducing accident risks.

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Publication Details

Journal
International Journal of Versatile Research and Analysis
Published
2026-05-01
DOI
https://doi.org/10.56975/ijvra.v4i5.705736
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
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article

AI - Assisted Driver Safety And Monitoring System

A Chelsea, Dr B Babypriya, Dr M Mohammadha Hussaini
International Journal of Versatile Research and Analysis
Sleep and Work-Related Fatigue
article

AI - Assisted Driver Safety And Monitoring System

A Chelsea, Dr B Babypriya, Dr M Mohammadha Hussaini
article en

Abstract

Road accidents remain a significant global concern, causing injuries and fatalities primarily due to driver drowsiness, alcohol consumption, and the lack of real-time monitoring systems. This paper presents an AI-Assisted Driver Safety and Monitoring System designed to enhance road safety through intelligent detection and alert mechanisms. The proposed system integrates multiple sensors, including the MQ2 gas sensor for alcohol detection, the ADXL345 accelerometer for motion and tilt analysis, and a vibration sensor for collision detection. An ESP32-CAM module is utilized for real-time video streaming and image-based processing to detect driver drowsiness. The system employs AI-based decision-making to analyze both sensor data and visual inputs, enabling early detection of abnormal conditions. Upon detection, alerts are generated through a buzzer and mobile notifications, and live monitoring is enabled. The proposed system aims to prevent accidents proactively rather than reacting after occurrence, thereby improving overall driver safety and reducing accident risks.

International Journal of Versatile Research and AnalysisVol. 4(5)
Government College of Science (PK)
Good health and well-being
Openalex Percentile: Top 8%
Sleep and Work-Related Fatigue
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AI - Assisted Driver Safety And Monitoring System — A Chelsea, Dr B Babypriya, et al. · International Journal of Versatile Research and Analysis (2026) | TGRS Research Map | TGRS