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 [email protected] 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.

翻译：协同感知对于自动驾驶至关重要，但始终受限于紧张的通信带宽。先前的研究通过使用固定速率编码器压缩完整特征图来降低带宽，但这种方法难以适应动态变化的环境；随后演变为空间选择方法，通过聚焦显著区域来提高效率，但这种以对象为中心的方法常常牺牲全局上下文，削弱了对场景的整体理解。为了克服这些局限性，我们提出了 \textit{WhisperNet}，这是一个具有带宽感知能力的框架，它引入了一种新颖的、以接收者为中心的范式，用于实现跨智能体的全局协调。发送方生成轻量级的显著元数据，而接收方则制定一个全局请求计划，该计划动态地为不同智能体和特征分配特征贡献预算，仅检索信息量最大的特征。随后，一个协同特征路由模块在融合前对齐相关消息，以确保结构一致性。大量实验表明，WhisperNet 实现了最先进的性能，在 OPV2V 数据集上将 [email protected] 提升了 2.4%，而通信成本仅为原来的 0.5%。作为一个即插即用组件，它仅需完整带宽的 5% 即可显著提升强基线模型的性能，并在定位噪声下保持鲁棒性。这些结果表明，对共享 \textit{内容} 和 \textit{位置} 进行全局协调分配是实现高效协同感知的关键。