arXiv:2606.17080v1 Announce Type: cross 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 capture
The continuous drive towards safer and more reliable autonomous driving systems necessitates larger, richer datasets for HD map construction, which existing public offerings do not sufficiently provide.
A large-scale, high-definition vector map dataset like HRDX directly addresses a major bottleneck in autonomous vehicle development, enabling accelerated research and deployment of advanced navigation systems.
The availability of HRDX changes the landscape for autonomous driving research by providing unprecedented scale and data richness, allowing for more robust model training and validation, and potentially faster progression to Level 4/5 autonomy.
- · Autonomous vehicle developers
- · AI/ML researchers in computer vision
- · Mapping companies
- · Logistics and transportation sectors
- · Companies with proprietary, less comprehensive mapping solutions
- · Competitors reliant on smaller or less diverse datasets
Improved performance and safety of autonomous driving systems due to enhanced HD map data.
Accelerated commercialization and adoption of self-driving cars and trucks, influencing urban planning and transportation infrastructure.
Potential for new regulations and standards concerning HD map data sharing and accuracy across different regions, impacting global AV deployment strategies.
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Read at arXiv cs.AI