Microservices and Containerization-Enabled Cloud-Native AI Pipelines for Secure Predictive V2V Communication Using RAG Cybersecurity
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Abstract
The evolution of connected and autonomous vehicles requires robust, low-latency, and secure vehicle-to-vehicle (V2V) communication frameworks. This paper presents a cloud-native AI pipeline architecture, enhanced with microservices and containerization, to enable predictive V2V communication while ensuring cybersecurity through RAG (Red, Amber, Green)-driven threat monitoring. The framework integrates AI models for real-time traffic prediction, vehicle behavior forecasting, and anomaly detection, providing proactive decision-making capabilities for autonomous vehicles. Microservices and containerization facilitate modularity, scalability, and flexible deployment across heterogeneous edge and cloud environments, while RAG-driven cybersecurity ensures continuous monitoring, risk assessment, and mitigation of potential cyber threats. Experimental evaluation demonstrates improved prediction accuracy, reduced communication latency, and robust security compliance, establishing a secure and efficient foundation for next-generation cooperative autonomous driving systems.
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