Papers › V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

20 Mar 2022arXiv:2203.10638archive 2025-07-28

Runsheng Xu, Hao Xiang, Zhengzhong Tu, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spatial relationships. These key modules are designed in a unified Transformer architecture to handle common V2X challenges, including asynchronous information sharing, pose errors, and heterogeneity of V2X components. To validate our approach, we create a large-scale V2X perception dataset using CARLA and OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The code is available at https://github.com/DerrickXuNu/v2x-vit.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2203.10638")

Code

Syntology Ran 0 of 3 code samples harvested from 1 repository linked to this paper; 3 have no recorded run.

By repository: official repository: 3 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

DerrickXuNu/v2x-vit officialmentioned in papermentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

3 samples harvested; 0 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3unverified

Licence: 0 of the 3 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from DerrickXuNu/v2x-vit. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

load_point_pillar_params DerrickXuNu/v2x-vit/v2xvit/hypes_yaml/yaml_utils.py official repository unverified MIT (permissive) · 0619a274e6eb1850 · report
load_voxel_params DerrickXuNu/v2x-vit/v2xvit/hypes_yaml/yaml_utils.py official repository unverified MIT (permissive) · 9bc229f7a9f4df89 · report
load_yaml DerrickXuNu/v2x-vit/v2xvit/hypes_yaml/yaml_utils.py official repository unverified MIT (permissive) · c40c6561b37d440c · report

Tasks

3D Object DetectionAutonomous VehiclesObject Detectionobject-detection

Datasets

Introduced by this paper, per the archive.

V2XSet

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Object Detection V2X-SIM V2X-ViT mAOE 0.383 #2 of 5 Archive leaderboard report
3D Object Detection V2X-SIM V2X-ViT mAP 22.4 #2 of 5 Archive leaderboard report
3D Object Detection V2X-SIM V2X-ViT mASE 0.250 #2 of 5 Archive leaderboard report
3D Object Detection V2X-SIM V2X-ViT mATE 0.848 #2 of 5 Archive leaderboard report
3D Object Detection V2XSet V2X-ViT AP0.5 (Noisy) 0.836 #1 of 6 Archive leaderboard report
3D Object Detection V2XSet V2X-ViT AP0.5 (Perfect) 0.882 #1 of 6 Archive leaderboard report
3D Object Detection V2XSet V2X-ViT AP0.7 (Noisy) 0.614 #1 of 6 Archive leaderboard report
3D Object Detection V2XSet V2X-ViT AP0.7 (Perfect) 0.712 #1 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPECARLADense ConnectionsDropoutEntropy RegularizationLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPPOPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections