AI-Enhanced Neural Network-Enabled Cyber-Physical Pipelines for Vehicle-to-Infrastructure Integration with Microservices and Containerization
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606004Keywords:
AI-driven systems, neural networks, cyber-physical pipelines, vehicle-to-infrastructure (V2I), microservices, containerization, intelligent transportation, autonomous vehicles, real-time communication, smart mobilityAbstract
The rapid growth of intelligent transportation systems requires secure, scalable, and adaptive solutions for seamless vehicle-to-infrastructure (V2I) integration. This paper presents an AI-enhanced neural network-enabled cyber-physical pipeline designed to optimize V2I communication, decision-making, and real-time data processing. The proposed framework leverages microservices architecture and containerization to ensure modularity, scalability, and resilience in heterogeneous traffic environments. Neural networks are employed to enable predictive analytics, anomaly detection, and adaptive control, while AI-driven optimization improves system performance under dynamic conditions. By combining cyber-physical pipelines with cloud-native deployment strategies, the framework enhances interoperability, reduces latency, and supports large-scale deployment of autonomous and connected vehicles. Experimental validation demonstrates the effectiveness of the approach in achieving low-latency communication, high scalability, and robust performance, making it a promising solution for next-generation smart transportation ecosystems.
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