Collaborative perception has garnered significant attention as a crucial technology to overcome the perceptual limitations of single-agent systems. Many state-of-the-art (SOTA) methods have achieved communication efficiency and high performance via intermediate fusion. However, they share a critical vulnerability: their performance degrades under adverse communication conditions due to the misalignment induced by data transmission, which severely hampers their practical deployment. To bridge this gap, we re-examine different fusion paradigms, and recover that the strengths of intermediate and late fusion are not a trade-off, but a complementary pairing. Based on this key insight, we propose CoRA, a novel collaborative robust architecture with a hybrid approach to decouple performance from robustness with low communication. It is composed of two components: a feature-level fusion branch and an object-level correction branch. Its first branch selects critical features and fuses them efficiently to ensure both performance and scalability. The second branch leverages semantic relevance to correct spatial displacements, guaranteeing resilience against pose errors. Experiments demonstrate the superiority of CoRA. Under extreme scenarios, CoRA improves upon its baseline performance by approximately 19% in AP@0.7 with more than 5x less communication volume, which makes it a promising solution for robust collaborative perception.

翻译：协作感知作为克服单智能体系统感知局限性的关键技术，已引起广泛关注。许多先进方法通过中间融合实现了通信效率和高性能。然而，它们存在一个关键缺陷：在恶劣通信条件下，由于数据传输引起的错位，其性能会下降，这严重阻碍了实际部署。为弥补这一差距，我们重新审视了不同的融合范式，并发现中间融合与后期融合的优势并非权衡关系，而是互补配对。基于这一关键见解，我们提出了CoRA，一种新颖的协作鲁棒架构，采用混合方法以低通信成本实现性能与鲁棒性的解耦。它由两个组件构成：特征级融合分支和对象级校正分支。其第一分支选择关键特征并高效融合，以确保性能和可扩展性。第二分支利用语义相关性校正空间位移，保证对姿态误差的鲁棒性。实验证明了CoRA的优越性。在极端场景下，CoRA在AP@0.7指标上较基线性能提升约19%，同时通信量减少超过5倍，这使其成为鲁棒协作感知的有前景解决方案。