{"url":"/dataset/tbv-dataset","name":"TbV Dataset","full_name":"Trust, but Verify Dataset","description_markdown":"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. \r\n\r\n- Over 1000 scenarios (\"logs\") captured by a fleet of autonomous vehicles.\r\n- 200 logs include real-world lane geometry or crosswalk changes, where an HD map has become stale.\r\n- Each log represents a continuous observation of a scene around a self-driving vehicle.\r\n- 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. \r\n- Captured across 4 seasons in six diverse cities (Austin, TX, Detroit, MI, Miami, FL, Palo Alto, CA, Pittsburgh, PA, and\r\nWashington, D.C.)\r\n- Includes 559.4K LiDAR Sweeps.\r\n- Includes 7.8M Images.\r\n- 15.5 hours of driving data.\r\n- 180 miles of driving (by the ego-vehicle).","description_withheld":null,"homepage":"http://tbv-dataset.github.io/","introduced_date":"2022-12-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/trust-but-verify-cross-modality-fusion-for-hd","title":"Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection","first_author":"John Lambert","url":null},"license":{"name":"CC BY NC-SA 4.0","url":"https://github.com/johnwlambert/tbv/blob/main/LICENSE"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"HD semantic map learning","url":"/task/hd-semantic-map-learning","datasets_with_task":"/datasets/task/hd-semantic-map-learning"}],"languages":[],"variants":["TbV Dataset"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}