HRDX: A Large-Scale Vector HD-Map Dataset

2026-06-11

AI Summary

Existing high-definition (HD) map datasets are limited in scale, semantic richness, and multimodal support, hindering long-horizon autonomous driving map construction. This work proposes HRDX, a large-scale vectorized HD map dataset spanning 1,400 kilometers of roadways, integrating six-camera imagery, 128-beam LiDAR, RTK GNSS/IMU, and precisely aligned aerial orthophotos. It annotates ten map element classes and over twenty semantic topological attributes. Aerial imagery is innovatively leveraged as a structural prior, and a composite scoring metric is introduced to jointly evaluate geometric and attribute accuracy. The dataset supports multimodal bird’s-eye-view (BEV) fusion and learning with privileged information. Experiments demonstrate that HRDX substantially improves online vector map construction performance, with aerial imagery enhancing geometric fidelity and enabling knowledge distillation to transfer these gains to purely vision-based models.

Abstract

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

Problem

• HD-map
• large-scale dataset
• semantic attributes
• aerial imagery
• autonomous driving

Innovation

• vector HD map
• aerial orthoimagery
• multimodal fusion
• privileged information distillation
• Composite Score