Microservices and Containerization-Enabled Cloud-Native AI Pipelines for Secure Predictive V2V Communication Using RAG Cybersecurity
Main Article Content
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.
Article Details
Section
How to Cite
References
1. Huang, L., et al. (2020). “Edge-Cloud Collaborative Deep Learning for Traffic Flow Prediction.” IEEE Transactions on Intelligent Transportation Systems.
2. Dong Wang, Lihua Dai (2022). Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment. Journal of Engineering 5 (6):1-9.
3. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
4. Chen, T., et al. (2019). “Distributed AI Inference on Edge Devices for Autonomous Vehicles.” Proceedings of IEEE Intelligent Vehicles Symposium.
5. Alwar Rengarajan, Rajendran Sugumar (2016). Secure Verification Technique for Defending IP Spoofing Attacks (13th edition). International Arab Journal of Information Technology 13 (2):302-309.
6. Cherukuri, Bangar Raju. "Microservices and containerization: Accelerating web development cycles." (2020).
7. Wang, J., et al. (2018). “Deep Learning-Based Vehicle Trajectory Prediction for V2V Communication.” IEEE Communications Letters.
8. Gandhi, S. T. (2023). RAG-Driven Cybersecurity Intelligence: Leveraging Semantic Search for Improved Threat Detection. International Journal of Research and Applied Innovations, 6(3), 8889-8897.
9. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2023). Navigating digital privacy and security effects on student financial behavior, academic performance, and well-being. Data Analytics and Artificial Intelligence, 3(2), 235–246.
10. Sugumar R (2014) A technique to stock market prediction using fuzzy clustering and artificial neural networks. Comput Inform 33:992–1024
11. Lu, N., et al. (2014). “Connected Vehicles: Solutions and Challenges.” IEEE Internet of Things Journal.
12. Sommer, C., & Dressler, F. (2011). “Vehicular Network Simulation.” Synthesis Lectures on Mobile and Pervasive Computing.