This repository is a paper digest of recent advances in collaborative / cooperative / multi-agent perception for V2I / V2V / V2X autonomous driving scenario. Papers are listed in alphabetical order of the first character.

• [Method and Framework]

• [Dataset and Simulator]

Note: I find it hard to fairly compare all methods on each benchmark since some published results are obtained without specified training and testing settings, or even modified model architectures. In fact, many works evaluate all baselines under their own settings and report them. Therefore, it is probably to find inconsistency between papers. Hence, I discard the collection and reproducton of all the benchmarks in a previous update. If you are interested, you can find plenty of results in this archived version.

• (Survey) Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities [paper], V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges [paper], Towards Vehicle-to-Everything Autonomous Driving: A Survey on Collaborative Perception [paper], Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges [paper], A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation [paper]
• (Talk) Vehicle-to-Vehicle (V2V) Communication (Waabi CVPR 24 Tutorial on Self-Driving Cars) [video], Vehicle-to-Vehicle (V2V) Communication (Waabi CVPR 23 Tutorial on Self-Driving Cars) [video], The Ultimate Solution for L4 Autonomous Driving [video], When Vision Transformers Meet Cooperative Perception [video], Scene Understanding beyond the Visible [video], Robust Collaborative Perception against Communication Interruption [video], Collaborative and Adversarial 3D Perception for Autonomous Driving [video], Vehicle-to-Vehicle Communication for Self-Driving [video], Adversarial Robustness for Self-Driving [video], L4感知系统的终极形态：协同驾驶 [video], CoBEVFlow-解决车-车/路协同感知的时序异步问题 [video], 新一代协作感知Where2comm减少通信带宽十万倍 [video], 从任务相关到任务无关的多机器人协同感知 [video], 协同自动驾驶：仿真与感知 [video], 基于群体协作的超视距态势感知 [video]
• (Library) V2Xverse: A Codebase for V2X-Based Collaborative End2End Autonomous Driving [code] [doc], HEAL: An Extensible Framework for Open Heterogeneous Collaborative Perception [code] [doc], OpenCOOD: Open Cooperative Detection Framework for Autonomous Driving [code] [doc], CoPerception: SDK for Collaborative Perception [code] [doc], OpenCDA: Simulation Tool Integrated with Prototype Cooperative Driving Automation [code] [doc]
• (People) Runsheng Xu@UCLA [web], Hao Xiang@UCLA [web], Yiming Li@NYU [web], Zixing Lei@SJTU [web], Yifan Lu@SJTU [web], Siqi Fan@THU [web], Hang Qiu@Waymo [web], Dian Chen@UT Austin [web], Yen-Cheng Liu@GaTech [web], Tsun-Hsuan Wang@MIT [web]
• (Workshop) Co-Intelligence@ECCV'24 [web], CoPerception@ICRA'23 [web], ScalableAD@ICRA'23 [web]
• (Background) Current Approaches and Future Directions for Point Cloud Object Detection in Intelligent Agents [video], 3D Object Detection for Autonomous Driving: A Review and New Outlooks [paper], DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning [video], A Survey of Multi-Agent Reinforcement Learning with Communication [paper]

Note: {Related} denotes that it is not a pure collaborative perception paper but has related content.

• AR2VP (Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Segmentation
  • Input: Point Cloud
• CPPC (Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• CMP (CMP: Cooperative Motion Prediction with Multi-Agent Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Forecasting
  • Input: Point Cloud
• CoBEVFusion (CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Segmentation, Detection
  • Input: RGB Image, Point Cloud
• CoBEVGlue (Self-Localized Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• CoCMT (CoCMT: Towards Communication-Efficient Corss-Modal Transformer For Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real
  • Task: Detection
  • Input: RGB Image, Point Cloud
• CoDriving (Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real, V2X-Sim, DAIR-V2X
  • Task: Planning
  • Input: RGB Image, Point Cloud
• CoDrivingLLM (Towards Interactive and Learnable Cooperative Driving Automation: A Large Language Model-Driven Decision-making Framework) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: Unknown
  • Task: Planning
  • Input: Scene State
• CollaMamba (CollaMamba: Efficient Collaborative Perception with Cross-Agent Spatial-Temporal State Space Model) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, V2V4Real
  • Task: Detection
  • Input: Point Cloud
• CoMamba (CoMamba: Real-time Cooperative Perception Unlocked with State Space Models) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, V2V4Real
  • Task: Detection
  • Input: Point Cloud
• CooPre (CooPre: Cooperative Pretraining for V2X Cooperative Perception)
  • Mode: Early Collaboration, Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real, V2X-Real
  • Task: Detection
  • Input: Point Cloud
• CP-Guard+ (CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: CP-GuardBench, V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• CTCE (Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X-Seq, V2X-Sim
  • Task: Detection
  • Input: RGB Image
• Debrief (Talking Vehicles: Cooperative Driving via Natural Language) [paper&review] [code]
  • Mode: Late Collaboration
  • Dataset: TalkingVehiclesGym, CARLA (simulator)
  • Task: Planning
  • Input: Scene State
• DiffCP (DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Detection
  • Input: Point Cloud
• Direct-CP (Direct-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• LMMCoDrive (LMMCoDrive: Cooperative Driving with Large Multimodal Model) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: AMoD, CARLA (simulator)
  • Task: Planning
  • Input: Scene State
• MOT-CUP (Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation) [paper] [code]
  • Mode: Early Collaboration, Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Tracking
  • Input: Point Cloud
• RopeBEV (RopeBEV: A Multi-Camera Roadside Perception Network in Bird’s-Eye-View)
  • Mode: Intermediate Collaboration
  • Dataset: RoScenes
  • Task: Segmentation
  • Input: RGB Image
• ParCon (ParCon: Noise-Robust Collaborative Perception via Multi-Module Parallel Connection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• PragComm (Pragmatic Communication in Multi-Agent Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2X-Sim, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• QUEST (QUEST: Query Stream for Vehicle-Infrastructure Cooperative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X-Seq
  • Task: Detection
  • Input: RGB Image
• RCDN (RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-Based 3D Neural Modeling) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V-N
  • Task: Segmentation
  • Input: RGB Image
• SiCP (SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Detection
  • Input: Point Cloud
• STAMP (STAMP: Scalable Task- And Model-Agnostic Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real
  • Task: Segmentation, Detection
  • Input: Point Cloud
• UniV2X (End-to-End Autonomous Driving through V2X Cooperation) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: Planning, Occupancy, Segmentation, Tracking
  • Input: RGB Image
• VIMI (VIMI: Vehicle-Infrastructure Multi-View Intermediate Fusion for Camera-Based 3D Object Detection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: Detection
  • Input: RGB Image
• V2X-DGW (V2X-DGW: Domain Generalization for Multi-Agent Perception under Adverse Weather Conditions) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V-w, V2XSet-w
  • Task: Detection
  • Input: Point Cloud
• V2X-M2C (V2X-M2C: Efficient Multi-Module Collaborative Perception with Two Connections) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet
  • Task: Detection
  • Input: Point Cloud
• V2X-PC (V2X-PC: Vehicle-to-Everything Collaborative Perception via Point Cluster) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud

• CoHFF (Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V+
  • Task: Occupancy, Segmentation, Detection
  • Input: RGB Image
• CoopDet3D (TUMTraf V2X Cooperative Perception Dataset) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: TUMTraf-V2X
  • Task: Detection
  • Input: RGB Image, Point Cloud
• CodeFilling (Communication-Efficient Collaborative Perception via Information Filling with Codebook) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X, OPV2VH+
  • Task: Detection
  • Input: RGB Image, Point Cloud
• MRCNet (Multi-Agent Collaborative Perception via Motion-Aware Robust Communication Network) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• Hetecooper (Hetecooper: Feature Collaboration Graph for Heterogeneous Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real
  • Task: Detection
  • Input: Point Cloud
• Infra-Centric CP (Rethinking the Role of Infrastructure in Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet, V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• V2X-Graph (Learning Cooperative Trajectory Representations for Motion Forecasting) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X-Seq, DAIR-V2X-Traj
  • Task: Forecasting
  • Input: Vector Map

• HEAL (An Extensible Framework for Open Heterogeneous Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V-H, DAIR-V2X
  • Task: Detection
  • Input: RGB Image, Point Cloud

• CMiMC (What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• DI-V2X (DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• V2XFormer (DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DeepAccident
  • Task: Detection, Forecasting
  • Input: RGB Image

• MACP (MACP: Efficient Model Adaptation for Cooperative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real
  • Task: Detection
  • Input: Point Cloud

• FreeAlign (Robust Collaborative Perception without External Localization and Clock Devices) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud

• {Related} BEVHeight (BEVHeight: A Robust Framework for Vision-Based Roadside 3D Object Detection) [paper] [code]
  • Mode: No Collaboration (only infrastructure data)
  • Dataset: DAIR-V2X, V2X-Sim
  • Task: Detection
  • Input: RGB Image
• CoCa3D (Collaboration Helps Camera Overtake LiDAR in 3D Detection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V+, DAIR-V2X, CoPerception-UAV+
  • Task: Detection
  • Input: RGB Image
• FF-Tracking (V2X-Seq: The Large-Scale Sequential Dataset for the Vehicle-Infrastructure Cooperative Perception and Forecasting) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X-Seq
  • Task: Tracking
  • Input: Point Cloud

• CoBEVFlow (Robust Asynchronous Collaborative 3D Detection via Bird's Eye View Flow) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X, IRV2V
  • Task: Detection
  • Input: Point Cloud
• FFNet (Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• How2comm (How2comm: Communication-Efficient and Collaboration-Pragmatic Multi-Agent Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud

• CORE (CORE: Cooperative Reconstruction for Multi-Agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Segmentation, Detection
  • Input: Point Cloud
• HM-ViT (HM-ViT: Hetero-Modal Vehicle-to-Vehicle Cooperative Perception with Vision Transformer) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Detection
  • Input: RGB Image, Point Cloud
• ROBOSAC (Among Us: Adversarially Robust Collaborative Perception by Consensus) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• SCOPE (Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Segmentation, Detection
  • Input: Point Cloud
• TransIFF (TransIFF: An Instance-Level Feature Fusion Framework for Vehicle-Infrastructure Cooperative 3D Detection with Transformers) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• UMC (UMC: A Unified Bandwidth-Efficient and Multi-Resolution Based Collaborative Perception Framework) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• {Related} CO3 (CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving) [paper&review] [code]
  • Mode: Early Collaboration (for contrastive learning)
  • Dataset: DAIR-V2X
  • Task: Representation Learning
  • Input: Point Cloud

• BM2CP {BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities} [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, DAIR-V2X
  • Task: Detection
  • Input: RGB Image, Point Cloud

• DUSA (DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• FeaCo (FeaCo: Reaching Robust Feature-Level Consensus in Noisy Pose Conditions) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2V4Real
  • Task: Detection
  • Input: Point Cloud
• What2comm (What2comm: Towards Communication-Efficient Collaborative Perception via Feature Decoupling) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2XSet, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud

• AdaFusion (Adaptive Feature Fusion for Cooperative Perception Using LiDAR Point Clouds) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, CODD
  • Task: Detection
  • Input: Point Cloud

• CoAlign (Robust Collaborative 3D Object Detection in Presence of Pose Errors) [paper] [code]
  • Mode: Intermediate Collaboration, Late Collaboration
  • Dataset: OPV2V, V2X-Sim, DAIR-V2X
  • Task: Detection
  • Input: Point Cloud
• {Related} DMGM (Deep Masked Graph Matching for Correspondence Identification in Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: CAD
  • Task: Correspondence Identification
  • Input: RGBD Image
• Double-M Quantification (Uncertainty Quantification of Collaborative Detection for Self-Driving) [paper] [code]
  • Mode: Early Collaboration, Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• MAMP (Model-Agnostic Multi-Agent Perception Framework) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: OPV2V
  • Task: Detection
  • Input: Point Cloud
• MATE (Communication-Critical Planning via Multi-Agent Trajectory Exchange) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: AutoCastSim (simulator), CoBEV-Sim (simulator)
  • Task: Planning
  • Input: Point Cloud
• MPDA (Bridging the Domain Gap for Multi-Agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet
  • Task: Detection
  • Input: Point Cloud
• WNT (We Need to Talk: Identifying and Overcoming Communication-Critical Scenarios for Self-Driving) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: CoBEV-Sim (simulator)
  • Task: Planning
  • Input: Point Cloud

• Coopernaut (COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AutoCastSim (simulator)
  • Task: Planning
  • Input: Point Cloud
• {Related} LAV (Learning from All Vehicles) [paper] [code]
  • Mode: Late Collaboration (for training)
  • Dataset: CARLA (simulator)
  • Task: Planning, Detection (auxiliary supervision), Segmentation (auxiliary supervision)
  • Input: RGB Image, Point Cloud
• TCLF (DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: DAIR-V2X
  • Task: Detection
  • Input: RGB Image, Point Cloud

• Where2comm (Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, DAIR-V2X, V2X-Sim, CoPerception-UAV
  • Task: Detection
  • Input: Point Cloud

• SyncNet (Latency-Aware Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud
• V2X-ViT (V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet
  • Task: Detection
  • Input: Point Cloud

• CoBEVT (CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, nuScenes
  • Task: Segmentation, Detection
  • Input: RGB Image, Point Cloud
• STAR (Multi-Robot Scene Completion: Towards Task-Agnostic Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Segmentation, Detection
  • Input: Point Cloud

• IA-RCP (Robust Collaborative Perception against Communication Interruption) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• CRCNet (Complementarity-Enhanced and Redundancy-Minimized Collaboration Network for Multi-agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• AttFuse (OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: Detection
  • Input: Point Cloud
• MP-Pose (Multi-Robot Collaborative Perception with Graph Neural Networks) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-MAP
  • Task: Segmentation
  • Input: RGB Image

• DiscoNet (Learning Distilled Collaboration Graph for Multi-Agent Perception) [paper&review] [code]
  • Mode: Early Collaboration (teacher model), Intermediate Collaboration (student model)
  • Dataset: V2X-Sim
  • Task: Detection
  • Input: Point Cloud

• Adversarial V2V (Adversarial Attacks On Multi-Agent Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2V-Sim (not publicly available)
  • Task: Adversarial Attack
  • Input: Point Cloud

• MASH (Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: AirSim
  • Task: Segmentation
  • Input: RGB Image

• When2com (When2com: Multi-Agent Perception via Communication Graph Grouping) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-MAP
  • Task: Segmentation, Classification
  • Input: RGB Image

• DSDNet (DSDNet: Deep Structured Self-Driving Network) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: nuScenes, CARLA (simulator), ATG4D
  • Task: Planning
  • Input: Point Cloud
• V2VNet (V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2V-Sim (not publicly available)
  • Task: Detection, Forecasting
  • Input: Point Cloud

• Robust V2V (Learning to Communicate and Correct Pose Errors) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2V-Sim (not publicly available)
  • Task: Detection, Forecasting
  • Input: Point Cloud

• Who2com (Who2com: Collaborative Perception via Learnable Handshake Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-CP (has an asynchronous issue between views)
  • Task: Segmentation
  • Input: RGB Image
• MAIN (Enhancing Multi-Robot Perception via Learned Data Association) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim
  • Task: Segmentation
  • Input: RGB Image

Note: {Real} denotes that the sensor data is obtained by real-world collection instead of simulation.

• Adver-City (Adver-City: Open-Source Multi-Modal Dataset for Collaborative Perception Under Adverse Weather Conditions) [paper] [code] [project]
• CP-GuardBench (CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception) [paper&review] [code] [project]
• {Real} InScope (InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios) [paper] [code] [project]
• Multi-V2X (Multi-V2X: A Large Scale Multi-modal Multi-penetration-rate Dataset for Cooperative Perception) [paper] [code] [project]
• OPV2V-N (RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modeling) [paper] [code] [project]
• {Real} V2X-Real (V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception) [paper] [code] [project]

• {Real} HoloVIC (HoloVIC: Large-Scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative) [paper] [code] [project]
• {Real} Open Mars Dataset (Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset) [code] [paper] [project]
• {Real} RCooper (RCooper: A Real-World Large-Scale Dataset for Roadside Cooperative Perception) [paper] [code] [project]
• {Real} TUMTraf-V2X (TUMTraf V2X Cooperative Perception Dataset) [paper] [code] [project]

• {Real} H-V2X (H-V2X: A Large Scale Highway Dataset for BEV Perception) [paper] [code] [project]

• {Real} DAIR-V2X-Traj (Learning Cooperative Trajectory Representations for Motion Forecasting) [paper] [code] [project]

• OPV2V-H (An Extensible Framework for Open Heterogeneous Collaborative Perception) [paper&review] [code] [project]

• DeepAccident (DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving) [paper] [code] [project]

• CoPerception-UAV+ (Collaboration Helps Camera Overtake LiDAR in 3D Detection) [paper] [code] [project]
• OPV2V+ (Collaboration Helps Camera Overtake LiDAR in 3D Detection) [paper] [code] [project]
• {Real} V2V4Real (V2V4Real: A Large-Scale Real-World Dataset for Vehicle-to-Vehicle Cooperative Perception) [paper] [code] [project]
• {Real} DAIR-V2X-Seq (V2X-Seq: The Large-Scale Sequential Dataset for the Vehicle-Infrastructure Cooperative Perception and Forecasting) [paper] [code] [project]

• IRV2V (Robust Asynchronous Collaborative 3D Detection via Bird's Eye View Flow) [paper&review] [code] [project]

• Roadside-Opt (Optimizing the Placement of Roadside LiDARs for Autonomous Driving) [paper] [code] [project]

• {Real} DAIR-V2X-C Complemented (Robust Collaborative 3D Object Detection in Presence of Pose Errors) [paper] [code] [project]
• RLS (Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library) [paper] [code] [project]
• V2XP-ASG (V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception) [paper] [code] [project]

• AutoCastSim (COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles) [paper] [code] [project]
• {Real} DAIR-V2X (DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection) [paper] [code] [project]

• CoPerception-UAV (Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps) [paper&review] [code] [project]

• V2XSet (V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer) [paper] [code] [project]

• OPV2V (OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication) [paper] [code] [project]

• DOLPHINS (DOLPHINS: Dataset for Collaborative Perception Enabled Harmonious and Interconnected Self-Driving) [paper] [code] [project]

• V2X-Sim (V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving) [paper] [code] [project]

• CARLA (CARLA: An Open Urban Driving Simulator) [paper] [code] [project]