{"url":"/dataset/v2x-sim","name":"V2X-SIM","full_name":null,"description_markdown":"**V2X-Sim**, short for vehicle-to-everything simulation, is the a synthetic collaborative perception dataset in autonomous driving developed by AI4CE Lab at NYU and MediaBrain Group at SJTU to facilitate collaborative perception between multiple vehicles and roadside infrastructure. Data is collected from both roadside and vehicles when they are presented near the same intersection. With information from both the roadside infrastructure and vehicles, the dataset aims to encourage research on collaborative perception tasks.\r\n\r\nAlthough not collected from the real world, highly realistic traffic simulation software is used to ensure the representativeness of the dataset compared to real-world driving scenarios. To be more exact, the traffic flow of the recording files is managed by CARLA-SUMO co-simulation, and three town maps from CARLA are currently used to increase the diversity of the dataset.\r\n\r\nHere is a tutorial showing how to load the dataset: [https://ai4ce.github.io/V2X-Sim/tutorial.html](https://ai4ce.github.io/V2X-Sim/tutorial.html)","description_withheld":null,"homepage":"https://ai4ce.github.io/V2X-Sim/","introduced_date":"2022-02-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/v2x-sim-a-virtual-collaborative-perception","title":"V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving","first_author":"Yiming Li","url":null},"license":{"name":"Custom","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"RGB Video","url":"/datasets/modality/rgb-video"}],"tasks":[{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"}],"languages":[],"variants":["V2X-SIM"],"data_loaders":[],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-object-detection-on-v2x-sim","task":"3D Object Detection","dataset_variant":"V2X-SIM","rows":5,"metrics":["mAP","mATE","mASE","mAOE"],"first_row_in_archive_order":{"model":"QUEST","paper":"/paper/quest-query-stream-for-vehicle-infrastructure","metrics":{"mAOE":"0.390","mAP":"23.9","mASE":"0.259","mATE":"0.832"},"code_links":[{"title":"leofansq/QUEST","url":"https://github.com/leofansq/QUEST"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/quest-query-stream-for-vehicle-infrastructure","title":"QUEST: Query Stream for Practical Cooperative Perception","date":"2023-08-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/where2comm-communication-efficient","title":"Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps","date":"2022-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":8,"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."}