Collaborative Perception

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.

🔗Jump to: [Dataset and Simulator] [Method and Framework]

🌟Recommendation

Helpful Learning Resource:thumbsup::thumbsup::thumbsup:

• (Talk) Robust Collaborative Perception against Communication Interruption [video], Uncertainty Quantification of Collaborative Detection for Self-Driving [video], Collaborative and Adversarial 3D Perception for Autonomous Driving [video], Vehicle-to-Vehicle Communication for Self-Driving [video], Adversarial Robustness for Self-Driving [video], 2022 1st Cooperative Perception Workshop Playback [video], 基于群体协作的超视距态势感知 [video], 协同自动驾驶：仿真与感知 [video], 新一代协作感知Where2comm减少通信带宽十万倍 [video], 基于V2X的多源协同感知技术初探 [video], 面向车路协同的群智机器网络 [video], IACS 2023 协同感知PhD Sharing [video], CICV 2022 数据驱动的车路协同专题 [video]
• (Survey) Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges [paper], A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation [paper]
• (Library) 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], Yiming Li@NYU [web], Hang Qiu@Waymo [web]
• (Workshop) ICRA 2023 [web], MFI 2022 [web], ITSC 2020 [web]
• (Competition) VIC3D Object Detection Challenge 清华AIR-百度Apollo车路协同自动驾驶算法挑战赛 [info]
• (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]

Typical Collaboration Modes:handshake::handshake::handshake:

Possible Optimization Directions:fire::fire::fire:

Published Benchmark Results:rocket::rocket::rocket:

• V2XSet (consider vehicles and infrastructures, pose error and time delay)

Method	Source	Ideal [email protected]	Ideal [email protected]	Noisy [email protected]	Noisy [email protected]
MPDA [ICRA'23]	link	🏆73.4🌟	-	-	-
MVRF [PAAP'22]	link	🏆71.5⭐	🏆88.9🌟	🏆61.9🌟	🏆84.3🌟
V2X-ViT [ECCV'22]	link	71.2	🏆88.2⭐	🏆61.4⭐	🏆83.6⭐
DiscoNet [NeurIPS'21]	link	69.5	84.4	54.1	79.8
F-Cooper [SEC'19]	link	68.0	84.0	46.9	71.5
V2VNet [ECCV'20]	link	67.7	84.5	49.3	79.1
AttFuse [ICRA'22]	link	66.4	80.7	48.7	70.9
CoBEVT [CoRL'22]	link	66.0	84.9	54.3	81.1
Where2comm [NeurIPS'22]	link	65.4	85.5	53.4	82.0
===	===	===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	link	71.0	81.9	38.4	72.0
Late Fusion	link	62.0	72.7	30.7	54.9
No Fusion (Lower Bound)	link	40.2	60.6	40.2	60.6

• OPV2V (consider adaptation ability by a digital town with realistic configs)

Method	Source	Default [email protected]	Default [email protected]	Culver [email protected]	Culver [email protected]
AdaFusion [WACV'23]	link	🏆85.6🌟	🏆91.6⭐	🏆79.0🌟	🏆88.0⭐
FuseBEVT [CoRL'22]	link	🏆85.2⭐	-	-	-
V2VAM [Arxiv'22]	link	84.9	🏆92.0🌟	73.1	🏆89.3🌟
CoBEVT [CoRL'22]	link	83.6	91.4	74.8	87.7
DiscoNet [NeurIPS'21]	link	83.6	89.9	-	-
V2X-ViT [ECCV'22]	link	82.6	89.1	73.7	87.3
V2VNet [ECCV'20]	link	82.2	89.7	73.4	86.0
FPV-RCNN [RAL'22]	link	82.0	-	🏆76.3⭐	-
AttFuse [ICRA'22]	link	81.5	90.8	73.5	85.4
MAMP [ICRA'23]	link	81.3	-	-	-
F-Cooper [SEC'19]	link	79.0	88.7	72.8	84.6
V2VAM+LCRN [Arxiv'22]	link	78.3	88.7	70.9	87.1
===	===	===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	link	80.0	89.1	69.6	82.9
Late Fusion	link	78.1	85.8	66.8	79.9
No Fusion (Lower Bound)	link	60.2	67.9	47.1	55.7

• V2X-Sim 2.0 (multi-modality multi-agent data for detection, tracking and segmentation)

Method	Source	Detection [email protected]	Detection [email protected]
Where2comm [NeurIPS'22]	link	🏆74.1🌟	🏆83.8🌟
FPV-RCNN [RAL'22]	link	🏆72.1⭐	78.7
V2X-ViT [ECCV'22]	link	68.1	🏆79.2⭐
Double-M Quantification [ICRA'23]	link	66.4	70.4
DiscoNet [NeurIPS'21]	link	63.4	69.0
AttFuse [ICRA'22]	link	62.9	76.0
V2VNet [ECCV'20]	link	62.8	68.4
CoAlign [ICRA'23]	link	60.7	73.9
STAR [CoRL'22]	link	57.2	62.8
Robust V2V [CoRL'20]	link	56.0	69.3
F-Cooper [SEC'19]	link	51.3	62.7
MASH [IROS'21]	link	49.6	62.2
When2com [CVPR'20]	link	39.9	44.0
Who2com [ICRA'20]	link	39.9	44.0
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	link	67.0	70.4
Late Fusion	link	39.1	44.0
No Fusion (Lower Bound)	link	44.2	49.9

• The results above are directly borrowed from publicly accessible papers. Since some of the results here are reported by the following papers instead of the original ones, the most reliable data source links are also given. The best effort is tried to ensure that all the collected benchmark results are in the same training and testing settings (if provided).

Reproduced Benchmark Results:sweat_drops::sweat_drops::sweat_drops:

• OPV2V Default

Method	[email protected]	[email protected]	[email protected]
V2VNet [ECCV'20]	🏆84.6🌟	🏆94.2🌟	🏆94.7⭐
AdaFusion [WACV'23]	🏆83.6⭐	93.6	94.1
FuseBEVT [CoRL'22]	83.3	93.0	93.7
Where2comm [NeurIPS'22]	82.3	93.5	94.0
DiscoNet [NeurIPS'21]	82.3	93.4	94.2
V2X-ViT [ECCV'22]	81.5	🏆94.1⭐	🏆94.8🌟
F-Cooper [SEC'19]	81.4	93.4	94.2
AttFuse [ICRA'22]	81.2	93.1	93.8
Where2comm [NeurIPS'22]	80.7	92.2	92.9
When2com [CVPR'20]	75.6	89.5	90.1
Who2com [ICRA'20]	75.6	89.5	90.1
When2com [CVPR'20]	71.0	87.8	89.0
Who2com [ICRA'20]	66.9	86.0	87.3
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	85.0	94.6	95.4
Late Fusion	76.2	90.9	91.8
No Fusion (Lower Bound)	65.1	87.9	89.8

• OPV2V Culver

Method	[email protected]	[email protected]	[email protected]
V2VNet [ECCV'20]	🏆75.8🌟	🏆88.0🌟	🏆89.5🌟
DiscoNet [NeurIPS'21]	🏆73.7⭐	🏆87.2⭐	🏆88.7⭐
FuseBEVT [CoRL'22]	73.2	85.7	87.3
AttFuse [ICRA'22]	72.8	87.0	88.4
AdaFusion [WACV'23]	72.7	86.6	88.1
Where2comm [NeurIPS'22]	72.3	86.8	88.2
Where2comm [NeurIPS'22]	71.5	86.5	88.0
F-Cooper [SEC'19]	70.8	86.9	🏆88.7⭐
V2X-ViT [ECCV'22]	70.2	86.4	88.6
When2com [CVPR'20]	60.6	80.4	82.3
Who2com [ICRA'20]	60.6	80.4	82.3
When2com [CVPR'20]	58.7	79.1	81.5
Who2com [ICRA'20]	51.6	75.5	79.0
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	73.5	88.2	89.8
Late Fusion	64.9	86.4	89.5
No Fusion (Lower Bound)	57.2	79.7	83.4

• V2XSet Ideal

Method	[email protected]	[email protected]	[email protected]
V2VNet [ECCV'20]	🏆80.3🌟	🏆92.0⭐	🏆93.0⭐
DiscoNet [NeurIPS'21]	🏆78.9⭐	🏆92.0⭐	92.9
AdaFusion [WACV'23]	78.6	🏆92.1🌟	92.9
FuseBEVT [CoRL'22]	78.5	90.8	91.8
Where2comm [NeurIPS'22]	78.0	91.6	92.4
AttFuse [ICRA'22]	77.1	91.0	91.9
V2X-ViT [ECCV'22]	76.3	🏆92.1🌟	🏆93.3🌟
Where2comm [NeurIPS'22]	76.0	90.1	91.0
F-Cooper [SEC'19]	75.8	91.4	92.6
When2com [CVPR'20]	67.9	86.4	87.5
Who2com [ICRA'20]	67.9	86.4	87.5
When2com [CVPR'20]	61.1	83.0	84.9
Who2com [ICRA'20]	60.4	81.8	83.8
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	80.1	93.1	94.0
Late Fusion	67.4	87.2	89.3
No Fusion (Lower Bound)	57.9	83.5	86.6

• V2XSet Noisy

Method	[email protected]	[email protected]	[email protected]
V2VNet [ECCV'20]	🏆57.0🌟	🏆88.7🌟	🏆92.7🌟
AttFuse [ICRA'22]	🏆53.4⭐	86.3	90.2
V2X-ViT [ECCV'22]	53.2	88.0	🏆92.6⭐
DiscoNet [NeurIPS'21]	52.7	🏆88.2⭐	92.1
Where2comm [NeurIPS'22]	52.7	87.4	91.0
Where2comm [NeurIPS'22]	51.3	85.9	89.7
AdaFusion [WACV'23]	51.2	87.8	92.1
FuseBEVT [CoRL'22]	51.1	85.9	89.8
F-Cooper [SEC'19]	50.4	86.5	90.8
When2com [CVPR'20]	48.2	81.4	85.2
Who2com [ICRA'20]	48.2	81.4	85.2
When2com [CVPR'20]	41.9	77.7	83.3
Who2com [ICRA'20]	37.2	75.8	82.2
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	51.4	90.1	93.8
Late Fusion	40.3	77.2	86.4
No Fusion (Lower Bound)	57.9	83.5	86.6

• Joint Set

Method	[email protected]	[email protected]	[email protected]
V2VNet [ECCV'20]	🏆81.6🌟	🏆92.5🌟	🏆93.4🌟
AdaFusion [WACV'23]	🏆80.2⭐	91.6	92.5
DiscoNet [NeurIPS'21]	80.0	91.6	92.6
Where2comm [NeurIPS'22]	79.9	91.3	92.2
FuseBEVT [CoRL'22]	79.8	90.9	91.9
AttFuse [ICRA'22]	78.9	91.0	91.9
Where2comm [NeurIPS'22]	78.5	90.1	91.1
V2X-ViT [ECCV'22]	78.1	🏆92.1⭐	🏆93.4🌟
F-Cooper [SEC'19]	78.1	91.7	🏆92.8⭐
When2com [CVPR'20]	69.7	86.1	87.2
Who2com [ICRA'20]	69.7	86.1	87.2
When2com [CVPR'20]	64.1	84.3	85.9
Who2com [ICRA'20]	60.9	81.8	83.7
===	===	===	===
Early Fusion (Upper Bound) Cooper [ICDCS'19]	82.1	93.2	94.2
Late Fusion	73.8	89.6	91.2
No Fusion (Lower Bound)	62.8	84.4	86.8

• In Joint Set evaluation, the OPV2V test split (16 scenes), OPV2V test culver city split (4 scenes), OPV2V validation split (9 scenes), V2XSet test split (19 scenes) and V2XSet validation split (6 scenes) are combined together as a much larger evaluation dataset (totaling 54 different scenes) to allow more stable ranking. The evaluated models are trained on a joint set of OPV2V train split and V2XSet train split with ego vehicle shuffling to augment the data.
• By default, the message is broadcasted to all agents to form a fully connected communication graph. Considering collaboration efficiency and bandwidth constraint, Who2com, When2com and Where2comm further apply different strategies to prune the fully connected communication graph into a partially connected one during inference. Both fully connected mode and partially connected mode are evaluated here and the latter is marked in italic.
• For fair comparison, all methods adopt the identical one-stage training settings in ideal scenarios (i.e., no pose error or time delay) without weight fine-tuning and message compression, extra fusion modules (e.g., down-sampling convolution layers) of intermediate collaboration mode are simplified if not necessary to mitigate the concern about the actual performance gain. PointPillar is adopted as the backbone for all reproduced methods.
• Though the reproduction process is simple and quick (the whole round takes less than 2 days with only two 3090 GPUs), multiple advanced training strategies are applied, which may boost some performance and make the ranking not aligned with the original reports. The reproduction is just a straightforward and fair evaluation for representative collaborative perception methods. To know how the official results are obtained, please refer to the papers or codes collected below for more details, which could be helpful.

🔖Dataset and Simulator

CVPR 2023:tada::tada::tada:

• V2V4Real (V2V4Real: A Large-Scale Real-World Dataset for Vehicle-to-Vehicle Cooperative Perception) [paper] [code] [project]
• V2X-Seq (V2X-Seq: The Large-Scale Sequential Dataset for the Vehicle-Infrastructure Cooperative Perception and Forecasting) [paper] [code] [project]

ICRA 2023

• 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]

CVPR 2022:tada::tada::tada:

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

NeurIPS 2022:tada::tada::tada:

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

ECCV 2022:tada::tada::tada:

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

ICRA 2022

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

ACCV 2022

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

ICCV 2021:tada::tada::tada:

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

CoRL 2017:tada::tada::tada:

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

🔖Method and Framework

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

Selected Preprint

• {Related} CBR (Calibration-free BEV Representation for Infrastructure Perception) [paper] [code]
  • Mode: No Collaboration (only infrastructure data)
  • Dataset: DAIR-V2X
  • Task: 3D Detection
• FFNet (Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: 3D Detection
• ROBOSAC (Among Us: Adversarially Robust Collaborative Perception by Consensus) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 3D Detection
• UMC (UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim, OPV2V
  • Task: 3D Detection
• VIMI (VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: DAIR-V2X
  • Task: 3D Detection
• V2VLC (Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: 3D Detection

CVPR 2023:tada::tada::tada:

• {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: 3D Detection
• CoCa3D (Collaboration Helps Camera Overtake LiDAR in 3D Detection) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: 3D Detection
• FF-Tracking (V2X-Seq: The Large-Scale Sequential Dataset for the Vehicle-Infrastructure Cooperative Perception and Forecasting) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Seq
  • Task: 3D Tracking

ICLR 2023:tada::tada::tada:

• {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

WACV 2023

• AdaFusion (Adaptive Feature Fusion for Cooperative Perception Using LiDAR Point Clouds) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, CODD
  • Task: 3D Detection

ICRA 2023

• 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: 3D Detection
• {Related} DMGM (Deep Masked Graph Matching for Correspondence Identification in Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: CAD
  • Task: Correspondence Identification
• Double-M Quantification (Uncertainty Quantification of Collaborative Detection for Self-Driving) [paper] [code]
  • Mode: Early Collaboration, Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 3D Detection
• MAMP (Model-Agnostic Multi-Agent Perception Framework) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: OPV2V
  • Task: 3D Detection
• MATE (Communication-Critical Planning via Multi-Agent Trajectory Exchange) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: AutoCastSim (simulator), CoBEV-Sim (simulator)
  • Task: Planning
• MPDA (Bridging the Domain Gap for Multi-Agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet
  • Task: 3D Detection

CVPR 2022:tada::tada::tada:

• Coopernaut (COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AutoCastSim (simulator)
  • Task: Planning
• {Related} LAV (Learning from All Vehicles) [paper] [code]
  • Mode: Late Collaboration (for training)
  • Dataset: CARLA (simulator)
  • Task: Planning, 3D Detection (auxiliary supervision), 2D Segmentation (auxiliart supervision)
• TCLF (DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: DAIR-V2X
  • Task: 3D Detection

NeurIPS 2022:tada::tada::tada:

• Where2comm (Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, V2X-Sim, DAIR-V2X, CoPerception-UAVs
  • Task: 3D Detection

ECCV 2022:tada::tada::tada:

• SyncNet (Latency-Aware Collaborative Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 3D Detection
• V2X-ViT (V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2XSet
  • Task: 3D Detection

CoRL 2022:tada::tada::tada:

• CoBEVT (CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V, nuScenes
  • Task: 2D Segmentation, 3D Detection
• STAR (Multi-Robot Scene Completion: Towards Task-Agnostic Collaborative Perception) [paper&review] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 2D Segmentation, 3D Detection

IJCAI 2022

• IA-RCP (Robust Collaborative Perception against Communication Interruption) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 3D Detection

MM 2022

• CRCNet (Complementarity-Enhanced and Redundancy-Minimized Collaboration Network for Multi-agent Perception) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2X-Sim
  • Task: 3D Detection

ICRA 2022

• AttFuse (OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: OPV2V
  • Task: 3D Detection
• MP-Pose (Multi-Robot Collaborative Perception with Graph Neural Networks) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-MAP
  • Task: 2D Segmentation

NeurIPS 2021:tada::tada::tada:

• 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: 3D Detection

ICCV 2021:tada::tada::tada:

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

IROS 2021

• MASH (Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking) [paper] [code]
  • Mode: Late Collaboration
  • Dataset: AirSim (simulator)
  • Task: 2D Segmentation

CVPR 2020:tada::tada::tada:

• When2com (When2com: Multi-Agent Perception via Communication Graph Grouping) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-MAP
  • Task: 2D Segmentation, 3D Classification

ECCV 2020:tada::tada::tada:

• V2VNet (V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2V-Sim (not publicly available)
  • Task: 3D Detection, Motion Forecasting

CoRL 2020:tada::tada::tada:

• Robust V2V (Learning to Communicate and Correct Pose Errors) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: V2V-Sim (not publicly available)
  • Task: 3D Detection, Motion Forecasting

ICRA 2020

• Who2com (Who2com: Collaborative Perception via Learnable Handshake Communication) [paper] [code]
  • Mode: Intermediate Collaboration
  • Dataset: AirSim-CP (has an asynchronous issue between views)
  • Task: 2D Segmentation