{"url":"/dataset/ccd","name":"CCD","full_name":"Car Crash Dataset","description_markdown":"**Car Crash Dataset** (**CCD**) is collected for traffic accident analysis. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions), whether ego-vehicles involved, accident participants, and accident reason descriptions.\n\nSource: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)\nImage Source: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)","description_withheld":null,"homepage":"https://github.com/Cogito2012/CarCrashDataset","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/uncertainty-based-traffic-accident","title":"Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning","first_author":"Wentao Bao","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Understanding","url":"/task/video-understanding","datasets_with_task":"/datasets/task/video-understanding"},{"name":"Accident Anticipation","url":"/task/accident-anticipation","datasets_with_task":"/datasets/task/accident-anticipation"},{"name":"Future prediction","url":"/task/future-prediction","datasets_with_task":"/datasets/task/future-prediction"}],"languages":[],"variants":["CCD"],"data_loaders":[{"repo":"https://github.com/Cogito2012/CarCrashDataset","url":"https://github.com/Cogito2012/CarCrashDataset","frameworks":[]}],"num_papers_in_archive":18,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/accident-anticipation-on-ccd","task":"Accident Anticipation","dataset_variant":"CCD","rows":2,"metrics":["TTA","AP"],"first_row_in_archive_order":{"model":"DSTA","paper":"/paper/a-dynamic-spatial-temporal-attention-network","metrics":{"AP":"99.6","TTA":"4.87"},"code_links":[{"title":"monjurulkarim/DSTA","url":"https://github.com/monjurulkarim/DSTA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-dynamic-spatial-temporal-attention-network","title":"A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents","date":"2021-06-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/uncertainty-based-traffic-accident","title":"Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning","date":"2020-08-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":1,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":14,"samples_ran":2,"samples_unverified":12,"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."}