Deep Learning-Based Autonomous Driving System with OpenCV Integration

Author(s)	Yashraj S. Dube, Rushikesh R. Shinde, Akshay I. Chavhan
Country	India
Abstract	This paper presents the design and development of a cost-efficient, real-time autonomous driving prototype that leverages Raspberry Pi and deep learning techniques for intelligent navigation. The proposed system integrates the YOLOv5 object detection framework with OpenCV-based lane detection and a lightweight CNN for traffic light classification. An ultrasonic sensor module is used for obstacle proximity awareness, and all modules are combined into a unified architecture optimized for execution on embedded hardware. A Streamlit-based dashboard provides interactive feedback and monitoring. The system demonstrates strong performance in terms of detection accuracy and response time, validating its potential for use in low-cost driver assistance applications and retrofitting older vehicles.
Keywords	Autonomous Vehicle, Deep Learning, OpenCV, YOLO, Raspberry Pi, Lane Detection, Traffic Sign Recognition, Obstacle Detection
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-17
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4960
Short DOI	https://doi.org/g9kf6k

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International Journal on Science and Technology