Hyper-V2X uses a Bayesian hypernetwork with partial weight generation and V2X context embedding to produce calibrated epistemic and aleatoric uncertainty estimates for multi-agent BEV segmentation on the OPV2V benchmark.
Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds
2 Pith papers cite this work, alongside 381 external citations. Polarity classification is still indexing.
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Pith papers citing it
381
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative 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.
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Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation
Hyper-V2X uses a Bayesian hypernetwork with partial weight generation and V2X context embedding to produce calibrated epistemic and aleatoric uncertainty estimates for multi-agent BEV segmentation on the OPV2V benchmark.
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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.