AI 中文总结
本文提出闭包保真度视角，分析V2X协作感知中远程证据的必要性，并据此设计SemRD-V2X，通过精确预算BEV选择、信道压缩和有界推理，在V2XSet上以26.6倍特征压缩提升检测精度。
AI 中文摘要
车联万物（V2X）协作感知通过共享中间特征来提升三维检测性能，但密集的远程特征可能重复了自我智能体可在本地推断出的上下文。大多数通信高效设计通过经验方式优化掩码或编码，留下了一个更基本的问题：在给定接收方自身观测的情况下，哪些远程证据是不可或缺的？我们引入了关于自我条件远程感知的闭包保真度视角。在有限演绎抽象和明确条件下，其率失真函数在无冗余核心上可分解，且精确零失真率变为$P_A H(\pi_A)$。该分析提出了一个具体的设计原则：传输紧凑证据，并通过有界接收端推理恢复可推导上下文。在此原则指导下，SemRD-V2X是一个可操作的神经代理，结合了精确预算的BEV支持选择、逐点信道压缩以及标准融合前的掩码共享权重重建。在模拟V2XSet和真实世界DAIR-V2X上的实验验证了所提设计。在一台Tesla V100上，与本地复现的V2X-ViT-v1基线进行受控五次V2XSet对比中，SemRD-V2X将分析特征载荷减少了$26.6\times$，同时将AP@0.5/AP@0.7提升了4.13/8.57个百分点，平均计算延迟仅增加3.81%。这些结果将闭包保真度定位为通信高效协作感知的分析视角和可操作设计原则。
英文摘要
Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote evidence is indispensable given the receiver's own observation? We introduce a closure-fidelity perspective on ego conditioned remote perception. Under a finite deductive abstraction and explicit conditions, its rate--distortion function decomposes over an irredundant core, and the exact zero-distortion rate becomes $P_A H(π_A)$. This analysis suggests a concrete design principle: transmit compact evidence and recover derivable context with bounded receiver-side inference. Guided by this principle, SemRD-V2X is an operational neural proxy that combines exact-budget BEV support selection, pointwise channel compression, and masked shared-weight reconstruction before standard fusion. Experiments on simulated V2XSet and real-world DAIR-V2X validate the resulting design. In a controlled five-run V2XSet comparison against a locally reproduced V2X-ViT-v1 baseline on one Tesla V100, SemRD-V2X reduces the analytical feature payload by $26.6\times$ while improving AP@0.5/AP@0.7 by 4.13/8.57 points, with 3.81\% additional mean compute latency. These results position closure fidelity as both an analytical lens and an actionable design principle for communication-efficient cooperative perception.
