REAL-TIME PROCESSING IN AUTONOMOUS VEHICLE NETWORKS: A DISTRIBUTED EDGE-CLOUD ARCHITECTURE FOR ENHANCED AUTONOMOUS VEHICLE PERFORMANCE

Abstract

The rapid evolution of autonomous vehicle networks demands advanced real-time data processing capabilities to ensure safety, efficiency, and seamless operation. This article presents a novel approach to real-time processing within these networks, leveraging scalable, low-latency cloud architectures and edge computing to support instantaneous data analysis and decision-making. By distributing computational tasks across edge nodes and centralized cloud platforms, our solution minimizes latency, enhances data accuracy, and ensures robust vehicle-to-vehicle and vehicle-to-infrastructure communication. The system incorporates a predictive AI model that dynamically adapts to traffic conditions, sensor inputs, and external environmental factors, enabling autonomous vehicles to make highly reliable split-second decisions. This innovative approach addresses existing limitations in network latency, data synchronization, and computational constraints, offering a transformative leap forward in autonomous vehicle network performance. Experimental results demonstrate significant improvements in processing efficiency, system reliability, and predictive accuracy across diverse operational conditions, establishing new benchmarks for autonomous vehicle systems.