{"url":"/dataset/v2xset","name":"V2XSet","full_name":null,"description_markdown":"A large-scale V2X perception dataset using CARLA and OpenCDA","description_withheld":null,"homepage":"https://github.com/DerrickXuNu/v2x-vit","introduced_date":"2022-03-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/v2x-vit-vehicle-to-everything-cooperative","title":"V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer","first_author":"Runsheng Xu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["V2XSet"],"data_loaders":[],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-object-detection-on-v2xset","task":"3D Object Detection","dataset_variant":"V2XSet","rows":6,"metrics":["AP0.5 (Perfect)","AP0.7 (Perfect)","AP0.5 (Noisy)","AP0.7 (Noisy)"],"first_row_in_archive_order":{"model":"V2X-ViT","paper":"/paper/v2x-vit-vehicle-to-everything-cooperative","metrics":{"AP0.5 (Noisy)":"0.836","AP0.5 (Perfect)":"0.882","AP0.7 (Noisy)":"0.614","AP0.7 (Perfect)":"0.712"},"code_links":[{"title":"DerrickXuNu/v2x-vit","url":"https://github.com/DerrickXuNu/v2x-vit"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/v2x-ahd-vehicle-to-everything-cooperation-1","title":"V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network","date":"2023-10-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/v2x-vit-vehicle-to-everything-cooperative","title":"V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer","date":"2022-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-distilled-collaboration-graph-for","title":"Learning Distilled Collaboration Graph for Multi-Agent Perception","date":"2021-11-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/opv2v-an-open-benchmark-dataset-and-fusion","title":"OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication","date":"2021-09-16","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/v2vnet-vehicle-to-vehicle-communication-for","title":"V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction","date":"2020-08-17","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/f-cooper-feature-based-cooperative-perception","title":"F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds","date":"2019-09-13","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":1,"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."}