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This paper presents a GitOps-driven architecture for managing metadata in the context of automatic train operation (ATO) systems, addressing the challenges of annotation management and regulatory compliance. By employing Data-as-Code principles and integrating CI/CD pipelines with Static Site Generation, the authors create a streamlined workflow that enhances traceability and reduces operational overhead. The key result is a lightweight solution that not only simplifies the annotation process but also ensures high-quality, dynamic datasets essential for robust AI-based perception systems in railway operations.
A GitOps-driven approach revolutionizes metadata management for automatic train operations, slashing operational overhead while ensuring compliance and traceability.
Automatic train operation (ATO) at grade of automation 3 and above (GoA3-GoA4) requires robust AI-based perception systems capable of reliably detecting obstacles and railway-specific objects under real-world conditions. The effectiveness of these modern artificial intelligence approaches depends heavily on large-scale, high-quality, and highly dynamic annotated datasets. However, managing metadata, maintaining provenance, and tracking the iterative evolution of these annotations impose significant infrastructural and regulatory requirements. Existing monolithic data catalogs often suffer from massive operational overhead, poor integration into developer workflows, and severe documentation drift. This paper introduces an innovative, lightweight GitOps-based architecture for metadata management. By leveraging Data-as-Code principles, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and Static Site Generation (SSG), the proposed approach establishes a seamless, developer-centric workflow. This ensures an traceability, enforces strict regulatory compliance, and automatically generates a highly performant dataset overview.
