LACO introduces Iterative Latent Deliberation, Cross-Horizon Saliency Attribution, and Structured Semantic Knowledge Distillation to enable low-latency latent communication in collaborative driving while preserving performance in CARLA simulations.
Advances in neural information processing systems35, 4874–4886 (2022)
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.
VQSOP applies sparsity-exploiting vector quantization and a dual-branch refinement module to cut communication volume by up to 82x while claiming state-of-the-art 3D occupancy prediction performance.
citing papers explorer
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LACO: Adaptive Latent Communication for Collaborative Driving
LACO introduces Iterative Latent Deliberation, Cross-Horizon Saliency Attribution, and Structured Semantic Knowledge Distillation to enable low-latency latent communication in collaborative driving while preserving performance in CARLA simulations.
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C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation
M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.
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Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction
VQSOP applies sparsity-exploiting vector quantization and a dual-branch refinement module to cut communication volume by up to 82x while claiming state-of-the-art 3D occupancy prediction performance.