Real-Time Driver State and Surrounding Awareness System with Lane Departure, Drowsiness, and Blind-Spot Detection

Shital Amar Patil; Sampatrao H. More

doi:10.63363/aijfr.2025.v06i06.2306

Real-Time Driver State and Surrounding Awareness System with Lane Departure, Drowsiness, and Blind-Spot Detection

Author(s)	Ms. Shital Amar Patil, Mr. Sampatrao H. More
Country	India
Abstract	Road transportation in India is expanding at an unprecedented rate, with commercial vehicles such as trucks, lorries, and long-haul carriers forming a major part of the logistics network. However, despite the rapid growth in transportation demand, many of these vehicles still operate without modern driver-assistance features that are commonly available in newer BS6-compliant models. Older vehicles, especially those below BS4 standards, lack critical safety technologies such as lane departure warning, fatigue detection, and blind-spot monitoring. This technological gap increases the risk of accidents caused by lane drifting, driver drowsiness, and undetected side-zone obstacles. To address these issues, this research proposes a Real-Time Driver State and Surrounding Awareness System that integrates lane departure detection, driver drowsiness monitoring, and blind-spot detection into a single, low-cost platform. The system is designed using a Raspberry Pi–based architecture that combines computer vision algorithms, image processing techniques, and sensor fusion to analyze driving conditions continuously. The driver’s eye movement and blink patterns are monitored to detect early signs of fatigue, while lane lines are tracked to identify unintentional lane deviation. Ultrasonic or camera-based blind-spot detection modules are deployed to sense nearby vehicles or obstacles that fall outside the driver’s natural field of vision. When any abnormal condition is detected—such as drowsiness, lane drift, or a blind-spot intrusion—the system provides real-time audio and visual alerts, enabling the driver to respond promptly and avoid potential collisions. The proposed solution is cost-effective, easily deployable, and designed for retrofitting into existing vehicles without major modifications. By targeting India’s vast population of older commercial vehicles, this work aims to significantly enhance road safety, reduce driver-related accidents, and promote smarter, safer driving practices on Indian highways..
Keywords	Raspberry Pi, Camera Model, Python, Proximity Sensor.
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2025
Published On	2025-12-04
DOI	https://doi.org/10.63363/aijfr.2025.v06i06.2306
Short DOI	https://doi.org/hbdssb

View / Download PDF File

E-ISSN 3048-7641

doi

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About AIJFR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-79-39-59	editor@aijfr.com

Advanced International Journal for Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Real-Time Driver State and Surrounding Awareness System with Lane Departure, Drowsiness, and Blind-Spot Detection

Share this