{"url":"/dataset/radiate","name":"RADIATE","full_name":"RAdar Dataset In Adverse weaThEr","description_markdown":"**RADIATE** (**RAdar Dataset In Adverse weaThEr**) is new automotive dataset created by Heriot-Watt University which includes Radar, Lidar, Stereo Camera and GPS/IMU.\nThe data is collected in different weather scenarios (sunny, overcast, night, fog, rain and snow) to help the research community to develop new methods of vehicle perception.\nThe radar images are annotated in 7 different scenarios: Sunny (Parked), Sunny/Overcast (Urban), Overcast (Motorway), Night (Motorway), Rain (Suburban), Fog (Suburban) and Snow (Suburban). The dataset contains 8 different types of objects (car, van, truck, bus, motorbike, bicycle, pedestrian and group of pedestrians).\n\nSource: [https://github.com/marcelsheeny/radiate_sdk](https://github.com/marcelsheeny/radiate_sdk)\nImage Source: [https://github.com/marcelsheeny/radiate_sdk](https://github.com/marcelsheeny/radiate_sdk)","description_withheld":null,"homepage":"https://github.com/marcelsheeny/radiate_sdk","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/radiate-a-radar-dataset-for-automotive","title":"RADIATE: A Radar Dataset for Automotive Perception in Bad Weather","first_author":"Marcel Sheeny","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"Multiple Object Tracking","url":"/task/multiple-object-tracking","datasets_with_task":"/datasets/task/multiple-object-tracking"}],"languages":[],"variants":["RADIATE"],"data_loaders":[{"repo":"https://github.com/marcelsheeny/radiate_sdk","url":"https://github.com/marcelsheeny/radiate_sdk","frameworks":[]}],"num_papers_in_archive":24,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-object-detection-on-radiate","task":"2D Object Detection","dataset_variant":"RADIATE","rows":2,"metrics":["mAP@0.3"],"first_row_in_archive_order":{"model":"SIRA","paper":"/paper/sira-scalable-inter-frame-relation-and-1","metrics":{"mAP@0.3":"68.68±1.12"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multiple-object-tracking-on-radiate","task":"Multiple Object Tracking","dataset_variant":"RADIATE","rows":2,"metrics":["MOTA"],"first_row_in_archive_order":{"model":"SIRA","paper":"/paper/sira-scalable-inter-frame-relation-and-1","metrics":{"MOTA":"47.79"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sira-scalable-inter-frame-relation-and-1","title":"SIRA: Scalable Inter-frame Relation and Association for Radar Perception","date":"2024-11-04","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/exploiting-temporal-relations-on-radar","title":"Exploiting Temporal Relations on Radar Perception for Autonomous Driving","date":"2022-04-03","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"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."}