构建了1400公里的大规模矢量高精地图数据集，支持多模态融合与长时序自动驾驶研究。
HRDX: A Large-Scale Vector HD-Map Dataset
- 采集40小时、1400公里车程数据，含6摄像头、128线激光雷达与厘米级定位。
- 包含10类矢量地图元素和20+语义拓扑属性，标注精度高且结构完整。
- 引入航拍影像作为结构先验，显著提升地图几何精度，适合多模态学习研究者。
可靠自动驾驶依赖于几何准确、语义丰富且可扩展至长时序的矢量高精地图。然而，现有公开高精地图数据集规模有限，语义属性稀疏，缺乏航拍影像等多模态信息，限制了新研究方向。本文提出HRDX，一个大规模矢量高精地图构建数据集，覆盖约40小时（1,400公里）低重叠驾驶里程，是此前公开数据集的数倍。数据由六台同步环视相机、128束激光雷达及厘米级RTK GNSS/IMU采集，并辅以精确对齐的航拍正射影像。标注涵盖10类矢量地图要素，附带20余个语义与拓扑属性。为评估更丰富的本体，引入综合评分（CS），联合评估几何保真度与属性正确性。基准实验表明，HRDX的规模提升了在线矢量地图构建效果；对齐的航拍影像在训练和/或推理中使用，可显著改善几何质量；航拍增强教师模型能将部分优势传递给仅用摄像头的学生模型，无需增加推理时传感器开销。HRDX旨在支持大规模高精地图学习、多模态鸟瞰图融合及训练时特权信息的可复现研究。数据集与基准测试已开放于https://github.com/honda-research-institute/HRDX。
原文摘要 · Abstract (English)
Reliable autonomous driving requires vectorized HD maps that are geometrically accurate, semantically rich, and scalable to long-horizon driving. However, existing public HD map datasets are limited in scale, provide sparse semantic attributes, and lack modalities such as aerial imagery that could enable new research directions. We present HRDX, a large-scale dataset for vector HD-map construction, spanning about 40 hours (1,400 km) of minimally overlapping drives, which is several times larger than prior public HD map datasets. Data is captured using six synchronized surround cameras, a 128-beam LiDAR, and centimeter-level RTK GNSS/IMU, and is further complemented by precisely aligned aerial orthoimagery. Annotations cover 10 vector map classes, complemented with over 20 semantic and topological attributes. To evaluate this richer ontology, we introduce the Composite Score (CS) to jointly assess geometric fidelity and attribute correctness. Benchmark experiments show that HRDX's scale improves online vector-map construction, and that aligned aerial imagery provides a useful structural prior: using aerial imagery at training and/or inference improves geometric map quality, while aerial-augmented teachers can transfer part of this benefit to camera-only students without increasing inference-time sensor requirements. HRDX is intended to support reproducible research on large-scale HD-map learning, multimodal BEV fusion, and training-time privileged information. HRDX dataset and benchmarks are available at https://github.com/honda-research-institute/HRDX
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