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

翻译：可靠的自动驾驶需要矢量化高清地图，这些地图需具备几何精确性、语义丰富性，并能扩展到长距离驾驶场景。然而，现有的公开高清地图数据集规模有限、语义属性稀疏，且缺乏诸如航拍图像等可开拓新研究方向的数据模态。我们提出了HRDX——一个用于构建矢量化高清地图的大规模数据集，涵盖约40小时（1400公里）的最低重叠驾驶数据，其规模是此前公开高清地图数据集的数倍。数据通过六台同步环视摄像头、128线激光雷达以及厘米级RTK GNSS/IMU采集，并辅以精确配准的航空正射影像。标注涵盖了10种矢量地图类别，并补充了超过20种语义和拓扑属性。为评估这一更丰富的本体体系，我们引入了复合评分（Composite Score, CS）以联合评估几何精度与属性正确性。基准实验表明，HRDX的规模可提升在线矢量地图构建性能，且对齐的航空影像能提供有效的结构先验信息：在训练和/或推理阶段使用航空影像可提高几何地图质量，而经航空增强的教师模型可将部分优势迁移至仅依赖摄像头的学生模型，且不增加推理时的传感器需求。HRDX旨在支持大规模高清地图学习、多模态BEV融合以及训练时特权信息的可重复研究。HRDX数据集与基准测试已开源发布于https://github.com/honda-research-institute/HRDX