WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration

• URL: http://arxiv.org/abs/2603.01708v1
• Date: Mon, 02 Mar 2026 10:33:25 GMT
• Title: WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration
• Authors: Gong Chen, Chaokun Zhang, Xinyan Zhao,
• Abstract summary: Collaborative perception is vital for autonomous driving yet remains constrained by tight communication budgets.<n>We introduce textitWhisperNet, a bandwidth-aware framework that proposes a novel, receiver-centric paradigm for global coordination across agents.<n>We show that WhisperNet achieves state-of-the-art performance, improving AP@0.7 on OPV2V by 2.4% with only 0.5% of the communication cost.
• Score: 7.294662317293144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative perception is vital for autonomous driving yet remains constrained by tight communication budgets. Earlier work reduced bandwidth by compressing full feature maps with fixed-rate encoders, which adapts poorly to a changing environment, and it further evolved into spatial selection methods that improve efficiency by focusing on salient regions, but this object-centric approach often sacrifices global context, weakening holistic scene understanding. To overcome these limitations, we introduce \textit{WhisperNet}, a bandwidth-aware framework that proposes a novel, receiver-centric paradigm for global coordination across agents. Senders generate lightweight saliency metadata, while the receiver formulates a global request plan that dynamically budgets feature contributions across agents and features, retrieving only the most informative features. A collaborative feature routing module then aligns related messages before fusion to ensure structural consistency. Extensive experiments show that WhisperNet achieves state-of-the-art performance, improving AP@0.7 on OPV2V by 2.4\% with only 0.5\% of the communication cost. As a plug-and-play component, it boosts strong baselines with merely 5\% of full bandwidth while maintaining robustness under localization noise. These results demonstrate that globally-coordinated allocation across \textit{what} and \textit{where} to share is the key to achieving efficient collaborative perception.

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