mlsanityData to 14 Oct 2026

Collaborative Perception for Autonomous Driving: Current Status and Future Trend

arXiv 2208.10371 ↗First posted 22 Aug 2022cs.CV cs.ROcited by 10

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Abstract (arXiv)

Perception is one of the crucial module of the autonomous driving system, which has made great progress recently. However, limited ability of individual vehicles results in the bottleneck of improvement of the perception performance. To break through the limits of individual perception, collaborative perception has been proposed which enables vehicles to share information to perceive the environments beyond line-of-sight and field-of-view. In this paper, we provide a review of the related work about the promising collaborative perception technology, including introducing the fundamental concepts, generalizing the collaboration modes and summarizing the key ingredients and applications of collaborative perception. Finally, we discuss the open challenges and issues of this research area and give some potential further directions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 26 of its 33 references that are in the map. First posted on arXiv 22 Aug 2022.

Builds on

26 of 33 references.

References · 26

  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  2. 2016Learning Multiagent Communication with Backpropagationcited by 366
  3. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  4. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  5. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 175
  6. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  7. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  8. 2020When2com: Multi-Agent Perception via Communication Graph Groupingcited by 157
  9. 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
  10. 2020Who2com: Collaborative Perception via Learnable Handshake Communicationcited by 127
  11. 2019Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscited by 110
  12. 2017VAIN: Attentional Multi-agent Predictive Modelingcited by 53
14 more
  1. 2021Adversarial Attacks On Multi-Agent Communicationcited by 49
  2. 2020Learning to Communicate and Correct Pose Errorscited by 46
  3. 2021Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Drivingcited by 45
  4. 2020Cooperative LIDAR Object Detection via Feature Sharing in Deep Networkscited by 19
  5. 2021Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshakingcited by 16
  6. 2020Cooperative Perception with Deep Reinforcement Learning for Connected Vehiclescited by 14
  7. 2020Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operationscited by 11
  8. 2018Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approachcited by 10
  9. 2020Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learningcited by 9
  10. 2020LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGBcited by 7
  11. 2020Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensorscited by 5
  12. 2020Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detectioncited by 3
  13. 2018Efficient V2V Communication Scheme for 5G MmWave Hyper-Connected CAVscited by 1
  14. 2018Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applicationscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 4; 2026: 1.

10 papers.