Autonomous vehicles rely on detailed and accurate environmental information to operate safely. High definition (HD) maps offer a promising solution, but their high maintenance cost poses a significant barrier to scalable deployment. This challenge is addressed by online HD map construction methods, which generate local HD maps from live sensor data. However, these methods are inherently limited by the short perception range of onboard sensors. To overcome this limitation and improve general performance, recent approaches have explored the use of standard definition (SD) maps as prior, which are significantly easier to maintain. We propose SDTagNet, the first online HD map construction method that fully utilizes the information of widely available SD maps, like OpenStreetMap, to enhance far range detection accuracy. Our approach introduces two key innovations. First, in contrast to previous work, we incorporate not only polyline SD map data with manually selected classes, but additional semantic information in the form of textual annotations. In this way, we enrich SD vector map tokens with NLP-derived features, eliminating the dependency on predefined specifications or exhaustive class taxonomies. Second, we introduce a point-level SD map encoder together with orthogonal element identifiers to uniformly integrate all types of map elements. Experiments on Argoverse 2 and nuScenes show that this boosts map perception performance by up to +5.9 mAP (+45%) w.r.t. map construction without priors and up to +3.2 mAP (+20%) w.r.t. previous approaches that already use SD map priors. Code is available at https://github.com/immel-f/SDTagNet

翻译：自动驾驶车辆依赖详尽准确的环境信息以确保安全运行。高精地图提供了极具前景的解决方案，但其高昂的维护成本成为规模化部署的主要障碍。在线高精地图构建方法通过实时传感器数据生成局部高精地图，旨在应对这一挑战。然而，此类方法受限于车载传感器固有的短感知范围。为突破此限制并提升整体性能，近期研究探索使用维护成本显著降低的标准定义地图作为先验信息。本文提出SDTagNet，这是首个充分利用OpenStreetMap等广泛可用的SD地图信息以提升远距离检测精度的在线高精地图构建方法。我们的方法引入两项关键创新：首先，区别于先前工作，我们不仅整合了人工选择类别的折线SD地图数据，还融合了文本标注形式的附加语义信息。通过将自然语言处理提取的特征融入SD矢量地图标记，我们消除了对预定义规范或详尽类别分类体系的依赖。其次，我们提出点级SD地图编码器与正交元素标识符，以统一整合所有类型的地图要素。在Argoverse 2和nuScenes数据集上的实验表明，相较于无先验的地图构建方法，本方法将地图感知性能最高提升+5.9 mAP（+45%）；相较于已使用SD地图先验的现有方法，最高提升+3.2 mAP（+20%）。代码公开于：https://github.com/immel-f/SDTagNet