Paper Key : IRJ************562
Author: Aniket Maurya
Date Published: 06 Nov 2023
Abstract
The rapid progression of autonomous vehicle technology has necessitated advanced vehicle detection techniques for safe and efficient driving. This paper focuses on the implementation of the state-of-the-art YOLOv8 (You Only Look Once version 8) algorithm for vehicle detection in autonomous vehicles. YOLOv8, an evolution of the YOLO series, is renowned for its real-time object detection capabilities, providing optimal balance between speed and accuracy. In the context of autonomous driving, the algorithm not only identifies vehicles but also classifies them based on their categories, providing crucial environmental information for scene analysis in real-time. The YOLOv8-based model is trained with a comprehensive dataset comprising various driving conditions, ensuring high detection performance. The research aims to contribute to the safety and efficiency of autonomous driving systems by providing a robust and reliable vehicle detection mechanism. The findings can significantly aid in the development of smart cities and contribute to the broader field of artificial intelligence in transportation.
DOI LINK : 10.56726/IRJMETS45592 https://www.doi.org/10.56726/IRJMETS45592