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</head>
<body>
<div class="container">
<div class="header">
<h1><i class="fas fa-robot"></i> Awesome Multi-Agent Collaborative Perception</h1>
<p>Explore cutting-edge resources for Multi-Agent Collaborative Perception, Prediction, and Planning</p>
<div class="stats-bar">
<div class="stat-badge">
<span class="stat-number">200+</span>
<span>Papers</span>
</div>
<div class="stat-badge">
<span class="stat-number">25+</span>
<span>Datasets</span>
</div>
<div class="stat-badge">
<span class="stat-number">50+</span>
<span>Code Repos</span>
</div>
<div class="stat-badge">
<span class="stat-number">2025</span>
<span>Updated</span>
</div>
</div>
</div>
<div class="main-sections">
<div class="section-card perception-card" onclick="showContent('perception')">
<div class="section-icon">
<i class="fas fa-eye"></i>
</div>
<h2>🔍 Perception</h2>
<p>Multi-agent collaborative sensing, 3D object detection, semantic segmentation, and sensor fusion techniques for enhanced environmental understanding.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">80+</div>
<div class="stat-label">Papers</div>
</div>
<div class="stat">
<div class="stat-number">V2X</div>
<div class="stat-label">Focus</div>
</div>
<div class="stat">
<div class="stat-number">15+</div>
<div class="stat-label">Venues</div>
</div>
</div>
</div>
<div class="section-card tracking-card" onclick="showContent('tracking')">
<div class="section-icon">
<i class="fas fa-route"></i>
</div>
<h2>📍 Tracking</h2>
<p>Multi-object tracking, collaborative state estimation, uncertainty quantification, and temporal consistency across multiple agents.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">15+</div>
<div class="stat-label">Methods</div>
</div>
<div class="stat">
<div class="stat-number">MOT</div>
<div class="stat-label">Focus</div>
</div>
<div class="stat">
<div class="stat-number">5+</div>
<div class="stat-label">Datasets</div>
</div>
</div>
</div>
<div class="section-card prediction-card" onclick="showContent('prediction')">
<div class="section-icon">
<i class="fas fa-chart-line"></i>
</div>
<h2>🔮 Prediction</h2>
<p>Trajectory forecasting, motion prediction, behavior understanding, and cooperative planning for autonomous systems.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">25+</div>
<div class="stat-label">Papers</div>
</div>
<div class="stat">
<div class="stat-number">GNN</div>
<div class="stat-label">Core Tech</div>
</div>
<div class="stat">
<div class="stat-number">E2E</div>
<div class="stat-label">Systems</div>
</div>
</div>
</div>
<div class="section-card datasets-card" onclick="showContent('datasets')">
<div class="section-icon">
<i class="fas fa-database"></i>
</div>
<h2>📊 Datasets</h2>
<p>Real-world and simulated datasets for collaborative perception research, including benchmarks and evaluation protocols.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">25+</div>
<div class="stat-label">Datasets</div>
</div>
<div class="stat">
<div class="stat-number">Real</div>
<div class="stat-label">& Sim</div>
</div>
<div class="stat">
<div class="stat-number">3D</div>
<div class="stat-label">Labels</div>
</div>
</div>
</div>
<div class="section-card methods-card" onclick="showContent('methods')">
<div class="section-icon">
<i class="fas fa-cogs"></i>
</div>
<h2>⚙️ Methods</h2>
<p>Communication strategies, fusion techniques, robustness approaches, and learning paradigms for multi-agent systems.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">60+</div>
<div class="stat-label">Methods</div>
</div>
<div class="stat">
<div class="stat-number">Comm</div>
<div class="stat-label">Efficient</div>
</div>
<div class="stat">
<div class="stat-number">Robust</div>
<div class="stat-label">Defense</div>
</div>
</div>
</div>
<div class="section-card conferences-card" onclick="showContent('conferences')">
<div class="section-icon">
<i class="fas fa-university"></i>
</div>
<h2>🏛️ Conferences</h2>
<p>Top-tier venues, workshops, and publication trends in collaborative perception and multi-agent systems research.</p>
<div class="stats">
<div class="stat">
<div class="stat-number">10+</div>
<div class="stat-label">Venues</div>
</div>
<div class="stat">
<div class="stat-number">2025</div>
<div class="stat-label">Latest</div>
</div>
<div class="stat">
<div class="stat-number">Trend</div>
<div class="stat-label">Analysis</div>
</div>
</div>
</div>
</div>
<!-- Content Panels -->
<div id="perceptionPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-eye"></i> Collaborative Perception</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="search-container">
<input type="text" class="search-box" placeholder="Search perception papers..." onkeyup="filterPapers('perception')">
</div>
<div class="filter-buttons">
<button class="filter-btn active" onclick="filterByVenue('perception', 'all')">All</button>
<button class="filter-btn" onclick="filterByVenue('perception', 'CVPR 2025')">CVPR 2025</button>
<button class="filter-btn" onclick="filterByVenue('perception', 'ICLR 2025')">ICLR 2025</button>
<button class="filter-btn" onclick="filterByVenue('perception', 'AAAI 2025')">AAAI 2025</button>
<button class="filter-btn" onclick="filterByVenue('perception', 'NeurIPS')">NeurIPS</button>
</div>
<div id="perceptionPapers" class="papers-grid">
<!-- Papers will be populated by JavaScript -->
</div>
</div>
<div id="trackingPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-route"></i> Collaborative Tracking</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="search-container">
<input type="text" class="search-box" placeholder="Search tracking papers..." onkeyup="filterPapers('tracking')">
</div>
<div id="trackingPapers" class="papers-grid">
<!-- Papers will be populated by JavaScript -->
</div>
</div>
<div id="predictionPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-chart-line"></i> Collaborative Prediction</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="search-container">
<input type="text" class="search-box" placeholder="Search prediction papers..." onkeyup="filterPapers('prediction')">
</div>
<div id="predictionPapers" class="papers-grid">
<!-- Papers will be populated by JavaScript -->
</div>
</div>
<div id="datasetsPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-database"></i> Datasets & Benchmarks</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="search-container">
<input type="text" class="search-box" placeholder="Search datasets..." onkeyup="filterDatasets()">
</div>
<div class="filter-buttons">
<button class="filter-btn active" onclick="filterDatasetType('all')">All</button>
<button class="filter-btn" onclick="filterDatasetType('real')">Real-World</button>
<button class="filter-btn" onclick="filterDatasetType('simulation')">Simulation</button>
<button class="filter-btn" onclick="filterDatasetType('v2x')">V2X</button>
</div>
<table class="dataset-table" id="datasetsTable">
<thead>
<tr>
<th>Dataset</th>
<th>Year</th>
<th>Type</th>
<th>Agents</th>
<th>Size</th>
<th>Features</th>
<th>Access</th>
</tr>
</thead>
<tbody>
<!-- Dataset rows will be populated by JavaScript -->
</tbody>
</table>
</div>
<div id="methodsPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-cogs"></i> Methods & Techniques</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="search-container">
<input type="text" class="search-box" placeholder="Search methods..." onkeyup="filterPapers('methods')">
</div>
<div class="filter-buttons">
<button class="filter-btn active" onclick="filterMethodType('all')">All</button>
<button class="filter-btn" onclick="filterMethodType('communication')">Communication</button>
<button class="filter-btn" onclick="filterMethodType('robustness')">Robustness</button>
<button class="filter-btn" onclick="filterMethodType('learning')">Learning</button>
</div>
<div id="methodsPapers" class="papers-grid">
<!-- Methods will be populated by JavaScript -->
</div>
</div>
<div id="conferencesPanel" class="content-panel">
<div class="panel-header">
<h2 class="panel-title"><i class="fas fa-university"></i> Conferences & Venues</h2>
<button class="close-btn" onclick="hideContent()">
<i class="fas fa-times"></i>
</button>
</div>
<div id="conferencesContent">
<!-- Conference content will be populated by JavaScript -->
</div>
</div>
</div>
<button class="back-to-top" id="backToTop" onclick="scrollToTop()">
<i class="fas fa-arrow-up"></i>
</button>
<script>
// Sample data - in a real implementation, this would come from your data sources
const perceptionPapers = [
{
title: "CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness",
venue: "CVPR 2025",
description: "Novel approach for efficient collaborative perception using supply-demand awareness and intermediate-late hybridization.",
paper: "https://arxiv.org/abs/2503.03430",
code: "https://github.com/Xu2729/CoSDH",
project: null
},
{
title: "V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection",
venue: "CVPR 2025",
description: "Cooperative fusion of LiDAR and 4D radar sensors for enhanced 3D object detection with denoising diffusion.",
paper: "https://arxiv.org/abs/2411.08402",
code: "https://github.com/ylwhxht/V2X-R",
project: null
},
{
title: "STAMP: Scalable Task- And Model-Agnostic Collaborative Perception",
venue: "ICLR 2025",
description: "Framework for scalable collaborative perception that is both task and model agnostic.",
paper: "https://openreview.net/forum?id=8NdNniulYE",
code: "https://github.com/taco-group/STAMP",
project: null
},
{
title: "Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps",
venue: "NeurIPS 2022",
description: "Groundbreaking work on efficient collaborative perception using spatial confidence maps for selective communication.",
paper: "https://openreview.net/forum?id=dLL4KXzKUpS",
code: "https://github.com/MediaBrain-SJTU/where2comm",
project: null
},
{
title: "CoBEVFlow: Robust Asynchronous Collaborative 3D Detection via Bird's Eye View Flow",
venue: "NeurIPS 2023",
description: "Handles temporal asynchrony in collaborative perception using bird's eye view flow.",
paper: "https://openreview.net/forum?id=UHIDdtxmVS",
code: "https://github.com/MediaBrain-SJTU/CoBEVFlow",
project: null
},
{
title: "UniV2X: End-to-End Autonomous Driving through V2X Cooperation",
venue: "AAAI 2025",
description: "Complete end-to-end system for autonomous driving with V2X cooperation.",
paper: "https://arxiv.org/abs/2404.00717",
code: "https://github.com/AIR-THU/UniV2X",
project: null
}
];
const trackingPapers = [
{
title: "MOT-CUP: Multi-Object Tracking with Conformal Uncertainty Propagation",
venue: "Preprint",
description: "Collaborative multi-object tracking with conformal uncertainty propagation for robust state estimation.",
paper: "https://arxiv.org/abs/2303.14346",
code: "https://github.com/susanbao/mot_cup",
project: null
},
{
title: "DMSTrack: Probabilistic 3D Multi-Object Cooperative Tracking",
venue: "ICRA 2024",
description: "Probabilistic approach for 3D multi-object cooperative tracking using differentiable multi-sensor Kalman filter.",
paper: "https://arxiv.org/abs/2309.14655",
code: "https://github.com/eddyhkchiu/DMSTrack",
project: null
},
{
title: "CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust",
venue: "ICRA 2025",
description: "Dynamic feature trust modulus for robust asynchronous collaborative perception.",
paper: "https://arxiv.org/abs/2502.08169",
code: "https://github.com/CrazyShout/CoDynTrust",
project: null
}
];
const predictionPapers = [
{
title: "V2X-Graph: Learning Cooperative Trajectory Representations",
venue: "NeurIPS 2024",
description: "Graph neural networks for learning cooperative trajectory representations in multi-agent systems.",
paper: "https://arxiv.org/abs/2311.00371",
code: "https://github.com/AIR-THU/V2X-Graph",
project: null
},
{
title: "Co-MTP: Cooperative Trajectory Prediction Framework",
venue: "ICRA 2025",
description: "Multi-temporal fusion framework for cooperative trajectory prediction in autonomous driving.",
paper: "https://arxiv.org/abs/2502.16589",
code: "https://github.com/xiaomiaozhang/Co-MTP",
project: null
},
{
title: "V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion",
venue: "Preprint",
description: "Spatio-temporal fusion approach for multi-agent perception and prediction in V2X systems.",
paper: "https://arxiv.org/abs/2412.01812",
code: "https://github.com/Zewei-Zhou/V2XPnP",
project: null
}
];
const datasets = [
{
name: "DAIR-V2X",
year: "2022",
type: "real",
agents: "V2I",
size: "71K frames",
features: ["3D boxes", "Multi-modal", "Infrastructure"],
link: "https://github.com/AIR-THU/DAIR-V2X"
},
{
name: "V2V4Real",
year: "2023",
type: "real",
agents: "V2V",
size: "20K frames",
features: ["3D boxes", "Real V2V", "Highway"],
link: "https://github.com/ucla-mobility/V2V4Real"
},
{
name: "TUMTraf-V2X",
year: "2024",
type: "real",
agents: "V2X",
size: "2K sequences",
features: ["Dense labels", "Cooperative", "Urban"],
link: "https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit"
},
{
name: "OPV2V",
year: "2022",
type: "simulation",
agents: "V2V",
size: "Large-scale",
features: ["CARLA", "Multi-agent", "Benchmark"],
link: "https://github.com/DerrickXuNu/OpenCOOD"
},
{
name: "V2X-Sim",
year: "2021",
type: "simulation",
agents: "Multi",
size: "Scalable",
features: ["Multi-agent", "Collaborative", "Synthetic"],
link: "https://github.com/ai4ce/V2X-Sim"
}
];
const methodsPapers = [
{
title: "ACCO: Is Discretization Fusion All You Need?",
venue: "Preprint",
description: "Investigation of discretization fusion techniques for collaborative perception efficiency.",
paper: "https://arxiv.org/abs/2503.13946",
code: "https://github.com/sidiangongyuan/ACCO",
project: null,
category: "communication"
},
{
title: "CP-Guard: Malicious Agent Detection and Defense",
venue: "AAAI 2025",
description: "Comprehensive framework for detecting and defending against malicious agents in collaborative perception.",
paper: "https://arxiv.org/abs/2412.12000",
code: null,
project: null,
category: "robustness"
},
{
title: "HEAL: Extensible Framework for Heterogeneous Collaborative Perception",
venue: "ICLR 2024",
description: "Open framework for heterogeneous collaborative perception with extensive customization options.",
paper: "https://openreview.net/forum?id=KkrDUGIASk",
code: "https://github.com/yifanlu0227/HEAL",
project: null,
category: "learning"
}
];
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