Design and Prototyping of Advanced Driver Assistance System

Author(s)	Prathamesh Vasekar, Lakhan Chavan, Gayatri Mane, Prajwal Pawar, Dr. S. S. Pimpale, Dr. Pruthviraj D. Patil
Country	India
Abstract	This article introduces a practical approach to road lane and object detection for an autonomous car prototype, leveraging OpenCV and Python. The lane detection system applies pre-processing techniques like edge detection and Hough Transform to accurately identify lane boundaries in real-time. For object detection, the model integrates YOLO via OpenCV’s DNN module to detect vehicles, pedestrians, and other obstacles. The system is powered by an Arduino-based motor control setup with a laptop handling image processing instead of a Raspberry Pi. Experimental tests demonstrate its ability to function under varying road and lighting conditions, proving its potential for real-world driver assistance and autonomous navigation. Future enhancements include integrating LiDAR/Radar for improved obstacle detection and transitioning to deep learning-based lane tracking.
Keywords	Lane Detection; Object Detection; OpenCV & YOLO
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-03
DOI	https://doi.org/10.71097/IJSAT.v16.i2.5891
Short DOI	https://doi.org/g9m28x

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International Journal on Science and Technology