AI 中文总结
针对全自动铁路运行的标注数据管理难题，提出基于GitOps的轻量级元数据管理架构，结合Data-as-Code、CI/CD与SSG，实现以开发者为中心的工作流，保障可追溯性与合规性并自动生成数据集概览。
AI 中文摘要
自动化等级3及以上（GoA3-GoA4）的自动列车运行（ATO）需要基于AI的鲁棒感知系统，能在真实场景下可靠检测障碍物和铁路专用物体。这类现代AI方法的有效性高度依赖大规模、高质量、高动态的标注数据集。然而，管理元数据、维护溯源、追踪标注的迭代演化，带来了重大的基础设施和监管要求。现有单体数据目录常存在大量运营开销、与开发者工作流集成差、文档严重漂移等问题。本文提出一种创新的轻量级GitOps架构用于元数据管理，通过利用数据即代码（Data-as-Code）原则、持续集成/持续部署（CI/CD）流水线和静态站点生成（SSG），该方法建立了以开发者为中心的无缝工作流，确保可追溯性、强化严格合规性，并自动生成高性能的数据集概览。
英文摘要
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.
Journal ref16th International Conference on Advanced Computer Information Technologies in Zlín, Czech Republic, 2026
