mlsanityData to 7 Oct 2026

V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions

arXiv 2403.11371 ↗First posted 17 Mar 2024cs.CVcited by 12

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Abstract (arXiv)

Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions with the domain gap. In this paper, we propose a Domain Generalization based approach, named textitV2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions. Our research aims to not only maintain favorable multi-agent performance in the clean weather but also promote the performance in the unseen adverse weather conditions by learning only on the clean weather data. To realize the Domain Generalization, we first introduce the Adaptive Weather Augmentation (AWA) to mimic the unseen adverse weather conditions, and then propose two alignments for generalizable representation learning: Trust-region Weather-invariant Alignment (TWA) and Agent-aware Contrastive Alignment (ACA). To evaluate this research, we add Fog, Rain, Snow conditions on two publicized multi-agent datasets based on physics-based models, resulting in two new datasets: OPV2V-w and V2XSet-w. Extensive experiments demonstrate that our V2X-DGW achieved significant improvements in the unseen adverse weathers. The code is available at https://github.com/Baolu1998/V2X-DGW.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 48 of its 65 references that are in the map. First posted on arXiv 17 Mar 2024.

Builds on

48 of 65 references.

References · 48

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36 more
  1. 2020Deep Domain-Adversarial Image Generation for Domain Generalisationcited by 231
  2. 2022CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformerscited by 227
  3. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  4. 2019Domain Generalization Using a Mixture of Multiple Latent Domainscited by 170
  5. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  6. 2023V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perceptioncited by 155
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  10. 2019Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscited by 110
  11. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 95
  12. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
  13. 2023CLIP the Gap: A Single Domain Generalization Approach for Object Detectioncited by 87
  14. 2024An Extensible Framework for Open Heterogeneous Collaborative Perceptioncited by 87
  15. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  16. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 72
  17. 2022Bridging the Domain Gap for Multi-Agent Perceptioncited by 60
  18. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  19. 2022Model-Agnostic Multi-Agent Perception Frameworkcited by 51
  20. 2021Adversarial Attacks On Multi-Agent Communicationcited by 48
  21. 2022Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communicationcited by 47
  22. 20233D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Cloudscited by 46
  23. 20213D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detectioncited by 43
  24. 2021Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detectioncited by 42
  25. 2022Domain Adaptive Object Detection for Autonomous Driving under Foggy Weathercited by 41
  26. 2023The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Researchcited by 27
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 7.

12 papers.

  1. 2024One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perceptioncited by 15
  2. 2025Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competitioncited by 9
  3. 2024TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weathercited by 7
  4. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  5. 2025CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perceptioncited by 3
  6. 2026CoopDiff: A Diffusion-Guided Approach for Cooperation under Corruptionscited by 1
  7. 20264D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weathercited by 1
  8. 2026Diff-KD: Diffusion-based Knowledge Distillation for Collaborative Perception under Corruptionscited by 0
  9. 2026Unsupervised Multi-agent and Single-agent Perception from Cooperative Viewscited by 0
  10. 2026CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalizationcited by 0
  11. 2026Domain-Generalized Adaptive Semantic Communication for Collaborative Perceptioncited by 0
  12. 2026VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulationcited by 0