AI-Based Driving Assist System Using Machine Learning and Computer Vision

• Author(s): Atharva Dhumal; Om Ghorpade; Nishad Gurav; Sahil Choudhari; Prof. R. R. Bhuvad
• Paper ID: 1711980
• Page: 938-944
• Published Date: 14-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Driving safety remains one of the most critical challenges due to human errors, distractions, and environmental conditions. This research presents an AI-based Driving Assist System that leverages computer vision and deep learning to detect lanes, traffic signs, pedestrians, and driver drowsiness in real time. Using convolutional neural networks (CNN), OpenCV, and machine learning algorithms, the system enhances situational awareness and provides real-time alerts to prevent accidents. The proposed system integrates multiple modules?lane detection, driver monitoring, and object detection?into a unified framework designed for affordability and adaptability. Experimental results demonstrate that the hybrid vision-based approach achieves high detection accuracy under varying conditions, contributing to safer and smarter driving.

Keywords

Driving Assist, Computer Vision, CNN, Lane Detection, Drowsiness Detection, AI, Deep Learning, Real-time Alerts.

Citations

IRE Journals:
Atharva Dhumal, Om Ghorpade, Nishad Gurav, Sahil Choudhari, Prof. R. R. Bhuvad "AI-Based Driving Assist System Using Machine Learning and Computer Vision" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 938-944 https://doi.org/10.64388/IREV9I5-1711980

IEEE:
Atharva Dhumal, Om Ghorpade, Nishad Gurav, Sahil Choudhari, Prof. R. R. Bhuvad "AI-Based Driving Assist System Using Machine Learning and Computer Vision" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1711980