# CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv: https://arxiv.org/abs/2503.03430 · HTML: https://mlsanity.com/paper/2503.03430/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 55 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 30 of 43)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [3D Object Detection with Pointformer](https://mlsanity.com/paper/2012.11409/) (2020)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [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)
- [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09738/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Fast Point R-CNN](https://mlsanity.com/paper/1908.02990/) (2019)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)

## Built on by (most cited first, 28 of 28)

- [SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception](https://mlsanity.com/paper/2508.13007/) (2025)
- [CoRA: A Collaborative Robust Architecture with Hybrid Fusion for Efficient Perception](https://mlsanity.com/paper/2512.13191/) (2025)
- [EIMC: Efficient Instance-aware Multi-modal Collaborative Perception](https://mlsanity.com/paper/2603.02532/) (2026)
- [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025)
- [JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception](https://mlsanity.com/paper/2511.17843/) (2025)
- [Linking Modality Isolation in Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2603.00609/) (2026)
- [RC-GeoCP: Geometric Consensus for 4D Radar-Camera Collaborative Perception](https://mlsanity.com/paper/2603.00654/) (2026)
- [WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration](https://mlsanity.com/paper/2603.01708/) (2026)
- [Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?](https://mlsanity.com/paper/2504.21774/) (2025)
- [Rate-Distortion Optimized Communication for Collaborative Perception](https://mlsanity.com/paper/2509.21994/) (2025)
- [CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation](https://mlsanity.com/paper/2510.13432/) (2025)
- [Background Fades, Foreground Leads: Curriculum-Guided Background Pruning for Efficient Foreground-Centric Collaborative Perception](https://mlsanity.com/paper/2510.19250/) (2025)
- [V2X-DSC: Multi-Agent Collaborative Perception with Distributed Source Coding Guided Communication](https://mlsanity.com/paper/2602.00687/) (2026)
- [C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation](https://mlsanity.com/paper/2607.01827/) (2026)
- [Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception](https://mlsanity.com/paper/2609.29456/) (2026)
- [Beyond BEV: Optimizing Point-Level Tokens for Collaborative Perception](https://mlsanity.com/paper/2508.19638/) (2025)
- [CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion](https://mlsanity.com/paper/2603.00682/) (2026)
- [When the City Teaches the Car: Label-Free 3D Perception from Infrastructure](https://mlsanity.com/paper/2603.16742/) (2026)
- [LACO: Adaptive Latent Communication for Collaborative Driving](https://mlsanity.com/paper/2605.22504/) (2026)
- [Adaptation-Free Heterogeneous Collaborative Perception with Unseen Agent Configurations](https://mlsanity.com/paper/2605.26642/) (2026)
- [UECP: Uncertainty-Enhanced Collaborative Perception](https://mlsanity.com/paper/2606.23046/) (2026)
- [CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization](https://mlsanity.com/paper/2606.31219/) (2026)
- [CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement](https://mlsanity.com/paper/2607.19036/) (2026)
- [HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework](https://mlsanity.com/paper/2607.26283/) (2026)
- [CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation](https://mlsanity.com/paper/2608.14085/) (2026)
- [HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perception](https://mlsanity.com/paper/2608.14603/) (2026)
- [CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms](https://mlsanity.com/paper/2609.16852/) (2026)
- [Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks](https://mlsanity.com/paper/2610.13298/) (2026)

## Abstract (arXiv)

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as mymethodname. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that mymethodname~ achieves state-of-the-art detection accuracy and optimal bandwidth trade-offs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://github.com/Xu2729/CoSDH.