Introduces a synchronized cross-view urban traffic dataset with drone ground truth for identity matching and monocular BEV localization tasks.
Tumtraf v2x cooperative perception dataset
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.
CarlaNCAP framework and 11k-frame dataset show infrastructure collective perception achieves up to 100% accident avoidance in EuroNCAP scenarios versus 33% for vehicle-only sensors.
Roadside LiDAR with human QA produces auditable near-miss evidence at urban intersections, showing lateral intrusion as the dominant conflict type in a heavy-vehicle–bicycle case while reducing common tracking failures.
citing papers explorer
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Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization
Introduces a synchronized cross-view urban traffic dataset with drone ground truth for identity matching and monocular BEV localization tasks.
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CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.
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CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios
CarlaNCAP framework and 11k-frame dataset show infrastructure collective perception achieves up to 100% accident avoidance in EuroNCAP scenarios versus 33% for vehicle-only sensors.
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Roadside LiDAR for Cooperative Safety Auditing at Urban Intersections: Toward Auditable V2X Infrastructure Intelligence
Roadside LiDAR with human QA produces auditable near-miss evidence at urban intersections, showing lateral intrusion as the dominant conflict type in a heavy-vehicle–bicycle case while reducing common tracking failures.