Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unable to support feature-level and raw-level collaborative algorithms due to their enormous bandwidth demands. In this paper, we propose DiffCP, a novel CP paradigm that utilizes a specialized diffusion model to efficiently compress the sensing information of collaborators. By incorporating both geometric and semantic conditions into the generative model, DiffCP enables feature-level collaboration with an ultra-low communication cost, advancing the practical implementation of CP systems. This paradigm can be seamlessly integrated into existing CP algorithms to enhance a wide range of downstream tasks. Through extensive experimentation, we investigate the trade-offs between communication, computation, and performance. Numerical results demonstrate that DiffCP can significantly reduce communication costs by 14.5-fold while maintaining the same performance as the state-of-the-art algorithm.

翻译：协同感知正逐渐成为解决独立智能系统固有局限性的有效方案。然而，当前无线通信系统由于带宽需求巨大，无法支持特征级与原始数据级的协同算法。本文提出DiffCP，一种新颖的协同感知范式，利用专用扩散模型高效压缩协作者的感知信息。通过将几何与语义条件同时融入生成模型，DiffCP能够以超低通信成本实现特征级协同，推动协同感知系统的实际部署。该范式可无缝集成至现有协同感知算法中，以增强各类下游任务性能。通过大量实验，我们深入探究了通信、计算与性能之间的权衡关系。数值结果表明，DiffCP在保持与最先进算法同等性能的同时，能够显著降低14.5倍的通信成本。