HIGH-AVAILABILITY DATABASE ARCHITECTURES FOR AUTONOMOUS VEHICLES: ENSURING REAL-TIME PERFORMANCE AND RELIABILITY

Abstract

This article presents a comprehensive analysis of database technologies critical for the operation of autonomous vehicles (AVs), focusing on high-availability architectures and real-time data processing capabilities. We examine the unique data requirements of AVs, including the challenges posed by the volume, velocity, variety, and veracity of data generated from various sensors and systems. The article investigates advanced database architectures, such as active-active and active-passive configurations, along with replication mechanisms and failover strategies to ensure continuous operation. We explore real-time data processing technologies, including stream processing frameworks and in-memory databases, and their integration with edge computing paradigms. Performance optimization techniques, including scalability approaches and latency reduction methods, are evaluated in the context of AV data management. Through case studies of industry leaders like Waymo and Tesla, we illustrate practical implementations of these technologies. The article concludes by discussing emerging trends, such as the role of AI and 5G networks in enhancing AV database systems, and addresses future challenges in security, privacy, and integration with smart infrastructure. Our findings underscore the critical role of advanced database technologies in enabling safe, efficient, and reliable autonomous vehicle operations, paving the way for future research and development in this rapidly evolving field.