用文本标注的普通地图提升自动驾驶高精地图构建精度
SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
- 融合地图文本注释与矢量数据，免去预设分类限制
- 点级编码器使各类地图元素统一融合，远距离感知提升45%
- 适合做在线高精地图构建的开发者和研究者
自动驾驶依赖详尽准确的环境信息。高精地图是可行方案，但维护成本高制约其大规模部署。在线高精地图构建方法利用实时传感器数据生成局部地图，但受限于车载传感器的短视距。为此，近期方法尝试使用易维护的标准定义（SD）地图作为先验。本文提出SDTagNet，首个全面利用广泛可用的SD地图（如OpenStreetMap）信息的在线高精地图构建方法，以提升远距离检测精度。核心创新有二：一、不仅使用人工选定类别的折线型SD地图数据，还引入文本注释形式的语义信息，通过NLP提取特征丰富地图标记，摆脱预设分类或完整类别体系的依赖；二、设计点级SD地图编码器与正交元素标识符，实现所有类型地图元素的统一融合。在Argoverse 2和nuScenes数据集上的实验表明，该方法相较无先验的地图构建提升高达+5.9 mAP（+45%），相较已有使用SD地图先验的方法提升+3.2 mAP（+20%）。代码已开源。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。