用信息论优化多智能体通信，大幅降低带宽占用同时提升感知精度。
Rate-Distortion Optimized Communication for Collaborative Perception
- 基于实用信息论设计通信策略，只传有用且无冗余的信息。
- 在两个数据集上实现顶尖感知性能，通信量减少最多108倍。
- 适合自动驾驶、机器人协同等带宽受限的实时感知场景。
协同感知通过多智能体共享视觉信息以增强环境理解，但受限于带宽资源。现有研究虽探索了任务性能与通信量的权衡，却缺乏理论基础。本文引入实用率失真理论，针对目标导向的多智能体系统，明确最优通信策略的两个关键条件：传递具有实际意义的信息，以及传输无冗余消息。基于此，提出RDcomm框架，包含两项创新：一是任务熵离散编码，根据任务相关性分配特征码字长度，提升信息供给效率；二是互信息驱动的消息选择，利用互信息神经估计逼近无冗余传输。在3D目标检测和鸟瞰图分割任务上的实验表明，RDcomm在DAIR-V2X和OPV2V数据集上达到当前最优精度，同时通信量减少最高达108倍。代码将公开。
原文摘要 · Abstract (English)
Collaborative perception emphasizes enhancing environmental understanding by enabling multiple agents to share visual information with limited bandwidth resources. While prior work has explored the empirical trade-off between task performance and communication volume, a significant gap remains in the theoretical foundation. To fill this gap, we draw on information theory and introduce a pragmatic rate-distortion theory for multi-agent collaboration, specifically formulated to analyze performance-communication trade-off in goal-oriented multi-agent systems. This theory concretizes two key conditions for designing optimal communication strategies: supplying pragmatically relevant information and transmitting redundancy-less messages. Guided by these two conditions, we propose RDcomm, a communication-efficient collaborative perception framework that introduces two key innovations: i) task entropy discrete coding, which assigns features with task-relevant codeword-lengths to maximize the efficiency in supplying pragmatic information; ii) mutual-information-driven message selection, which utilizes mutual information neural estimation to approach the optimal redundancy-less condition. Experiments on 3D object detection and BEV segmentation demonstrate that RDcomm achieves state-of-the-art accuracy on DAIR-V2X and OPV2V, while reducing communication volume by up to 108 times. The code will be released.
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