发表机构
School of Artificial Intelligence, University of Chinese Academy of Sciences; Institute of Automation, Chinese Academy of Sciences（中国科学院大学人工智能学院; 中国科学院自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CERF框架，通过引入Poture虚拟模态、卡尔曼滤波跟踪器与运动预测模型，在保持协同感知性能的同时降低95%通信开销，且无需重训练即可集成异构智能体。
AI 中文摘要
协同感知通过多个智能体间共享信息以获取全面的场景表示，提升单个智能体的感知能力。然而，现有多数方法依赖传输并融合密集特征图进行协同，会产生不可避免的通信开销与异构性挑战，限制了其在实际场景的部署。为应对这些挑战，我们提出CERF，一种用于开放异构协同感知的新型通信高效且无需重训练的框架。在CERF中，我们引入一种新的虚拟模态（名为Poture），由其他智能体的感知输出生成，以增强自身智能体提取的鸟瞰图（Bird's Eye View，BEV）特征。为缓解传输延迟，我们采用基于卡尔曼滤波器（Kalman-filter）的跟踪器与运动预测模型，从历史感知结果推导当前预测。大量实验表明，CERF在各类下游任务中实现了与主流中间协同方法相当的性能，同时降低了95%的通信开销。此外，CERF可将未知异构智能体无缝集成到现有协同框架中，无需额外重训练成本。代码可在该https URL获取。
英文摘要
Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual agents. However, most existing methods rely on transmitting and fusing dense feature maps for collaboration, which incurs inevitable communication overhead and heterogeneity challenges, limiting their practicality for real-world deployment. To address these challenges, we propose CERF, a novel Communication-Efficient and Retraining-Free framework for open heterogeneous collaborative perception. In CERF, we introduce a new virtual modality (termed Poture), which is generated from the perception outputs of other agents, to augment the extracted Bird's Eye View (BEV) features of the ego agent. To mitigate transmission delays, we employ a Kalman-filter based tracker and a motion forecasting model to derive the current predictions from historical perception results. Extensive experiments demonstrate that CERF achieves performance comparable to mainstream intermediate-collaboration methods while reducing communication overhead by 95% across various downstream tasks. Furthermore, CERF enables seamless integration of unknown heterogeneous agents into the existing collaborative framework without additional retraining costs. Code is available at https://github.com/uestchjw/CERF.
CommentsAccepted by ICASSP 2026
