# Collaborative Perception for Autonomous Driving: Current Status and Future Trend

arXiv: https://arxiv.org/abs/2208.10371 · HTML: https://mlsanity.com/paper/2208.10371/

## 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 (most cited first, 26 of 26)

- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World](https://mlsanity.com/paper/2006.09348/) (2020)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [VAIN: Attentional Multi-agent Predictive Modeling](https://mlsanity.com/paper/1706.06122/) (2017)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [Learning to Communicate and Correct Pose Errors](https://mlsanity.com/paper/2011.05289/) (2020)
- [Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving](https://mlsanity.com/paper/2109.11615/) (2021)
- [Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks](https://mlsanity.com/paper/2002.08440/) (2020)
- [Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking](https://mlsanity.com/paper/2107.00771/) (2021)
- [Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles](https://mlsanity.com/paper/2004.10927/) (2020)
- [Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations](https://mlsanity.com/paper/2011.08581/) (2020)
- [Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.09350/) (2018)
- [Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning](https://mlsanity.com/paper/2012.03414/) (2020)
- [LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB](https://mlsanity.com/paper/2006.09738/) (2020)
- [Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors](https://mlsanity.com/paper/2011.08317/) (2020)
- [Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection](https://mlsanity.com/paper/2010.11353/) (2020)
- [Efficient V2V Communication Scheme for 5G MmWave Hyper-Connected CAVs](https://mlsanity.com/paper/1802.10472/) (2018)
- [Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications](https://mlsanity.com/paper/1812.07777/) (2018)

## Built on by (most cited first, 10 of 10)

- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving](https://mlsanity.com/paper/2501.03535/) (2025)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection](https://mlsanity.com/paper/2503.17175/) (2025)
- [Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving](https://mlsanity.com/paper/2408.00374/) (2024)
- [BridgeTA: Bridging the Representation Gap in Knowledge Distillation via Teacher Assistant for Bird's Eye View Map Segmentation](https://mlsanity.com/paper/2508.09599/) (2025)
- [CST: Collaborative Selective Transmission for Communication-Efficient Multimodal Edge Inference](https://mlsanity.com/paper/2608.22115/) (2026)

## 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.