mlsanityData to 14 Oct 2026

Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective

arXiv 2504.04631 ↗First posted 6 Apr 2025cs.CVcited by 5

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Abstract (arXiv)

The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP's fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 79 of its 146 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

79 of 146 references.

References · 79

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38 more
  1. 2022CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformerscited by 227
  2. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  3. 2022BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Drivingcited by 191
  4. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
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  11. 2023V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecastingcited by 114
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  14. 2024An Extensible Framework for Open Heterogeneous Collaborative Perceptioncited by 87
  15. 2022Latency-Aware Collaborative Perceptioncited by 86
  16. 2023Collaboration Helps Camera Overtake LiDAR in 3D Detectioncited by 81
  17. 2023DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Drivingcited by 73
  18. 2023HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformercited by 70
  19. 2024TUMTraf V2X Cooperative Perception Datasetcited by 67
  20. 2023Asynchrony-Robust Collaborative Perception via Bird's Eye View Flowcited by 58
  21. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
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  25. 2022Uncertainty Quantification of Collaborative Detection for Self-Drivingcited by 46
  26. 2021Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Drivingcited by 45
  27. 2023CORE: Cooperative Reconstruction for Multi-Agent Perceptioncited by 41
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  29. 2023Among Us: Adversarially Robust Collaborative Perception by Consensuscited by 37
  30. 2023DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detectioncited by 34
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  38. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24

50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Drivingcited by 1
  2. 2026Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentationcited by 1
  3. 2026Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectivescited by 0
  4. 2026HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perceptioncited by 0
  5. 2026CoAnchor: Robust Collaborative Perception under Spatio-Temporal Misalignment via Object-Level Anchorscited by 0