Scalable Cloud-Native AI Pipelines for Autonomous Vehicle Learning and Image Denoising across Multi-Platform Oracle Environments
DOI:
https://doi.org/10.15680/IJCTECE.2023.0604004Keywords:
Cloud-native AI, microservices, autonomous vehicles, image denoising, Oracle Cloud, multi-platform learning, deep learning, containerization, scalability, distributed AI, adaptive pipelines, real-time analytics, model deployment, interoperability, secure computingAbstract
This paper presents a scalable cloud-native AI framework that leverages microservices and containerization to enable adaptive learning for autonomous vehicles across multi-platform Oracle environments. The proposed pipeline integrates image denoising modules using deep learning techniques to enhance visual perception accuracy under dynamic conditions such as noise, weather distortion, and low-light visibility. By utilizing Oracle’s cloud infrastructure, the system ensures secure, high-performance data processing, efficient model deployment, and real-time analytics for autonomous navigation. The architecture supports interoperability between heterogeneous systems, enabling seamless model updates, distributed training, and cross-platform learning optimization. Experimental results demonstrate improvements in image quality, inference accuracy, and system scalability, making the approach suitable for next-generation autonomous driving ecosystems.
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