A Deep Dive into Traffic Sign and Lane Monitoring Systems

This project focuses on developing an IoT and AI-based Driver Assistance System to enhance road safety through the real-time identification and monitoring of vehicles, obstacles, and hazards. The system includes continuous lane positioning, traffic sign recognition, and potential hazard identification using IoT sensors and AI algorithms. A significant improvement is the provision of real-time notifications, enabling the driver to minimize the risk of accidents by responding to identifiable dangers with minimal distraction. The system employs computer vision for lane detection, utilizes YOLOv5 for traffic sign detection, and incorporates Arduino-controlled sensors for obstacle detection. This adaptive solution aligns with smart-city agendas, facilitating data-oriented road safety solutions and integrating easily into existing traffic management systems. Future upgrades may include enhancements in security features and expanded functionality.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-03-13
DOI
https://doi.org/10.55041/ijsrem42022
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

A Deep Dive into Traffic Sign and Lane Monitoring Systems

Vinod Desai
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Autonomous Vehicle Technology and Safety
article

A Deep Dive into Traffic Sign and Lane Monitoring Systems

Vinod Desai
article en

Abstract

This project focuses on developing an IoT and AI-based Driver Assistance System to enhance road safety through the real-time identification and monitoring of vehicles, obstacles, and hazards. The system includes continuous lane positioning, traffic sign recognition, and potential hazard identification using IoT sensors and AI algorithms. A significant improvement is the provision of real-time notifications, enabling the driver to minimize the risk of accidents by responding to identifiable dangers with minimal distraction. The system employs computer vision for lane detection, utilizes YOLOv5 for traffic sign detection, and incorporates Arduino-controlled sensors for obstacle detection. This adaptive solution aligns with smart-city agendas, facilitating data-oriented road safety solutions and integrating easily into existing traffic management systems. Future upgrades may include enhancements in security features and expanded functionality.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(03)
Openalex Percentile: Top 96%
Autonomous Vehicle Technology and Safety
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A Deep Dive into Traffic Sign and Lane Monitoring Systems — Vinod Desai · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2025) | TGRS Research Map | TGRS