Integration of Edge Computing in Autonomous Vehicles for System Efficiency, Real-Time Data Processing, and Decision-Making for Advanced Transportation

Abstract

With the increasing advancement of technology in the automotive industry, autonomous vehicles (AVs) are becoming an integral part of the future of transportation. The rapid development of AVs is transforming the transportation sector, promising significant improvements in safety, efficiency, and convenience. However, the successful deployment of AVs depends on the ability to process vast amounts of data in real-time, ensuring swift decision-making and robust system performance. Edge computing has emerged as a critical technology in addressing these requirements by bringing computational resources closer to the data source, reducing latency and enhancing data processing capabilities. This paper explores the integration of edge computing into AV systems, focusing on technical architectures, data processing methodologies, and the resultant system efficiency. The study discusses various architectural frameworks that facilitate the seamless operation of AVs, including the use of distributed computing nodes and localized data centers. Additionally, the paper analyzes the data processing techniques necessary for handling the large datasets generated by AV sensors and the algorithms employed to ensure real-time decision-making. Finally, the impact of edge computing on system efficiency is examined, highlighting improvements in latency, bandwidth usage, and overall vehicle performance. The research aims to provide a detailed understanding of how edge computing can enhance the functionality and reliability of autonomous vehicles, supporting their widespread adoption.