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.

本文提出了一种系统化的向量地图注释框架（VMA），用于有效生成大规模自动驾驶场景的高清地图。VMA采用分治注释方案解决了高清地图生成中的空间可扩展性问题，并将地图要素抽象为不同的几何图案统一点序列表示，从而提高了生成地图的效率和灵活性。实测结果表明，VMA显著提高了地图生成效率，同时降低了人力成本。

VMA: 大规模自驾场景分治向量化地图注释系统