多智能体协同感知中的语用通信
Pragmatic Communication in Multi-Agent Collaborative Perception
AI总结：
本文针对多智能体协同感知中感知能力与通信成本的权衡问题，提出语用通信策略及PragComm系统，通过任务关键消息选择、字典近似表示和有益协作者筛选，在OPV2V等数据集上以极低通信量实现优于以往方法的检测与跟踪性能。
摘要：
协同感知使每个智能体能够通过与其他智能体交换消息来增强其感知能力。这本质上导致了感知能力与通信成本之间的权衡。以往的工作在智能体之间传输完整的全帧高维特征图，导致巨大的通信成本。为了提高通信效率，我们提出仅传输协作者下游任务所需的信息。这种语用通信策略聚焦于三个关键方面：i) 语用消息选择，从完整数据中选择任务关键部分，产生空间和时间上稀疏的特征向量；ii) 语用消息表示，通过任务自适应字典实现高维特征向量的语用近似，从而能够使用整数索引进行通信；iii) 语用协作者选择，识别有益的协作者，剪除不必要的通信链路。遵循这一策略，我们首先为感知-通信权衡构建了一个数学优化框架，然后提出了PragComm，一个多智能体协同感知系统，包含两个关键组件：i) 单智能体检测与跟踪，以及ii) 语用协作。所提出的PragComm促进了语用通信，并适应广泛的通信条件。我们在真实世界数据集V2V4Real以及仿真数据集OPV2V和V2X-SIM2.0上，对协同3D目标检测和跟踪任务评估了PragComm。在OPV2V上，PragComm持续优于以往方法，且通信量降低了超过32.7K倍。代码可在github.com/PhyllisH/PragComm获取。
英文摘要：
Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7K times lower communication volume on OPV2V. Code is available at github.com/PhyllisH/PragComm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
