High-definition (HD) maps are essential for autonomous driving systems. Traditionally, an expensive and labor-intensive pipeline is implemented to construct HD maps, which is limited in scalability. In recent years, crowdsourcing and online mapping have emerged as two alternative methods, but they have limitations respectively. In this paper, we provide a novel methodology, namely global map construction, to perform direct generation of vectorized global maps, combining the benefits of crowdsourcing and online mapping. We introduce GlobalMapNet, the first online framework for vectorized global HD map construction, which updates and utilizes a global map on the ego vehicle. To generate the global map from scratch, we propose GlobalMapBuilder to match and merge local maps continuously. We design a new algorithm, Map NMS, to remove duplicate map elements and produce a clean map. We also propose GlobalMapFusion to aggregate historical map information, improving consistency of prediction. We examine GlobalMapNet on two widely recognized datasets, Argoverse2 and nuScenes, showing that our framework is capable of generating globally consistent results.

翻译：高精地图对于自动驾驶系统至关重要。传统上，构建高精地图采用昂贵且劳动密集的流程，其可扩展性有限。近年来，众包与在线建图作为两种替代方法出现，但各自存在局限性。本文提出了一种新颖的方法论，即全局地图构建，以结合众包与在线建图的优势，直接生成矢量化的全局地图。我们介绍了GlobalMapNet，这是首个用于矢量化全局高精地图构建的在线框架，它能在自车上更新并利用一个全局地图。为了从零开始生成全局地图，我们提出了GlobalMapBuilder来持续匹配与融合局部地图。我们设计了一种新算法Map NMS，以移除重复的地图元素并生成清晰的地图。我们还提出了GlobalMapFusion来聚合历史地图信息，提升预测的一致性。我们在两个广泛认可的数据集Argoverse2和nuScenes上评估了GlobalMapNet，结果表明我们的框架能够生成全局一致的结果。