High-definition (HD) map serves as the essential infrastructure of autonomous driving. In this work, we build up a systematic vectorized map annotation framework (termed VMA) for efficiently generating HD map of large-scale driving scene. We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abstract map elements with a variety of geometric patterns as unified point sequence representation, which can be extended to most map elements in the driving scene. VMA is highly efficient and extensible, requiring negligible human effort, and flexible in terms of spatial scale and element type. We quantitatively and qualitatively validate the annotation performance on real-world urban and highway scenes, as well as NYC Planimetric Database. VMA can significantly improve map generation efficiency and require little human effort. On average VMA takes 160min for annotating a scene with a range of hundreds of meters, and reduces 52.3% of the human cost, showing great application value.

翻译：高清地图作为自动驾驶的关键基础设施。本文构建了一套系统的矢量化地图标注框架（VMA），用于高效生成大规模驾驶场景的高清地图。我们设计了一种分治标注方案来解决高清地图生成的空间可扩展性问题，并将具有多种几何形态的地图要素抽象为统一的点序列表示，该方法可扩展至驾驶场景中的大部分地图要素。VMA具备高效性与可扩展性，所需人工投入极少，且在空间尺度与要素类型上具有灵活性。我们通过真实城市与高速公路场景以及纽约市平面数据库，对标注性能进行了定量与定性验证。VMA可显著提升地图生成效率且所需人工干预极少。平均而言，VMA完成数百米范围场景的标注需时160分钟，并降低了52.3%的人力成本，展现出巨大应用价值。