{"url":"/dataset/road-anomaly","name":"Road Anomaly","full_name":null,"description_markdown":"This dataset contains images of unusual dangers which can be encountered by a vehicle on the road – animals, rocks, traffic cones and other obstacles. Its purpose is testing autonomous driving perception algorithms in rare but safety-critical circumstances.","description_withheld":null,"homepage":"https://www.epfl.ch/labs/cvlab/data/road-anomaly/","introduced_date":"2019-04-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/detecting-the-unexpected-via-image","title":"Detecting the Unexpected via Image Resynthesis","first_author":"Krzysztof Lis","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[],"variants":["Road Anomaly"],"data_loaders":[],"num_papers_in_archive":56,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-road-anomaly","task":"Anomaly Detection","dataset_variant":"Road Anomaly","rows":10,"metrics":["AP","FPR95"],"first_row_in_archive_order":{"model":"OodDINO","paper":null,"metrics":{"AP":"95.21","FPR95":"2.11"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/diffusion-for-out-of-distribution-detection","title":"Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond","date":"2024-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","date":"2023-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/residual-pattern-learning-for-pixel-wise-out","title":"Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation","date":"2022-11-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pixels-together-strong-segmenting-unknown","title":"RbA: Segmenting Unknown Regions Rejected by All","date":"2022-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/far-away-in-the-deep-space-nearest-neighbor","title":"Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection","date":"2022-11-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pixel-wise-energy-biased-abstention-learning","title":"Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes","date":"2021-11-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/standardized-max-logits-a-simple-yet","title":"Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation","date":"2021-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pixel-wise-anomaly-detection-in-complex","title":"Pixel-wise Anomaly Detection in Complex Driving Scenes","date":"2021-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/synthesize-then-compare-detecting-failures","title":"Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation","date":"2020-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":7,"samples_unverified":7,"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":5,"samples_harvested":32,"samples_ran":13,"samples_unverified":19,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":2,"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."}