mlsanityData to 14 Oct 2026

Learning to Communicate and Correct Pose Errors

arXiv 2011.05289 ↗First posted 10 Nov 2020cs.CV cs.LG cs.ROcited by 46

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Abstract (arXiv)

Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous messages they might receive. In this paper, we study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner. Despite a huge performance boost when the agents solve the task together, the gain is quickly diminished in the presence of pose noise since the communication relies on spatial transformations. Hence, we propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and finally, to reach a consensus about those errors. Experiments confirm that our proposed framework significantly improves the robustness of multi-agent self-driving perception and motion forecasting systems under realistic and severe localization noise.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 11; 2024: 14; 2025: 8; 2026: 5.

46 papers.

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  1. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  2. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  3. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  4. 2022Robust Collaborative 3D Object Detection in Presence of Pose Errorscited by 129
  5. 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
  6. 2024An Extensible Framework for Open Heterogeneous Collaborative Perceptioncited by 87
  7. 2022Latency-Aware Collaborative Perceptioncited by 86
  8. 2024Communication-Efficient Collaborative Perception via Information Filling with Codebookcited by 68
  9. 2023CORE: Cooperative Reconstruction for Multi-Agent Perceptioncited by 41
  10. 2023Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perceptioncited by 38
  11. 2023Among Us: Adversarially Robust Collaborative Perception by Consensuscited by 37
  12. 2023Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Drivingcited by 25
34 more
  1. 2023BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalitiescited by 20
  2. 2023S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Realitycited by 18
  3. 2023Vehicle-to-Everything Cooperative Perception for Autonomous Drivingcited by 15
  4. 2022A Cooperative Perception System Robust to Localization Errorscited by 14
  5. 2022Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Librarycited by 14
  6. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  7. 2024A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challengescited by 13
  8. 2024DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptionscited by 13
  9. 2024RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustmentcited by 12
  10. 2022Collaborative Perception for Autonomous Driving: Current Status and Future Trendcited by 10
  11. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  12. 2024EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 9
  13. 2024Robust Collaborative Perception without External Localization and Clock Devicescited by 8
  14. 2025V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Realitycited by 8
  15. 2023MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Drivingcited by 7
  16. 2023Optimizing the Placement of Roadside LiDARs for Autonomous Drivingcited by 7
  17. 2024UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detectioncited by 7
  18. 2025Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labelscited by 7
  19. 2024RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modelingcited by 6
  20. 2024AdvGPS: Adversarial GPS for Multi-Agent Perception Attackcited by 5
  21. 2024Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sourcescited by 5
  22. 2025Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspectivecited by 5
  23. 2026EIMC: Efficient Instance-aware Multi-modal Collaborative Perceptioncited by 4
  24. 2024Evaluating Roadside Perception for Autonomous Vehicles: Insights from Field Testingcited by 3
  25. 2025V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detectioncited by 3
  26. 2025CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perceptioncited by 3
  27. 2024Self-Localized Collaborative Perceptioncited by 2
  28. 2024Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A reviewcited by 1
  29. 2026SiMO: Single-Modality-Operable Multimodal Collaborative Perceptioncited by 1
  30. 2025Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Surveycited by 0
  31. 2025Communication-Efficient Multi-Agent 3D Detection via Hybrid Collaborationcited by 0
  32. 2026UECP: Uncertainty-Enhanced Collaborative Perceptioncited by 0
  33. 2026CoAnchor: Robust Collaborative Perception under Spatio-Temporal Misalignment via Object-Level Anchorscited by 0
  34. 2026SV2V-RSim: A Comprehensive Benchmark for Self-Selective V2V Cooperative Perception with Near-Realistic Datacited by 0