Year: 2025
Type: article
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.
Authors: Atharva Dhumal, Om Ghorpade, Nishad Gurav, Sahil Choudhari, Prof. R. R. Bhuvad
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416599719
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