TbV Dataset (Trust, but Verify Dataset)
Introduced by Lambert et al. in Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
The TbV dataset is large-scale dataset created to allow the community to improve the state of the art in machine learning tasks related to mapping, that are vital for self-driving.
- Over 1000 scenarios ("logs") captured by a fleet of autonomous vehicles.
- 200 logs include real-world lane geometry or crosswalk changes, where an HD map has become stale.
- Each log represents a continuous observation of a scene around a self-driving vehicle.
- On average, each scenario is 54 seconds in duration. Each scenario has an HD map representing lane boundaries, crosswalks, drivable area, and a raster map of ground height at 0.3 meter resolution.
- Captured across 4 seasons in six diverse cities (Austin, TX, Detroit, MI, Miami, FL, Palo Alto, CA, Pittsburgh, PA, and Washington, D.C.)
- Includes 559.4K LiDAR Sweeps.
- Includes 7.8M Images.
- 15.5 hours of driving data.
- 180 miles of driving (by the ego-vehicle).
Papers
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