  Collaborative perception can substantially boost each agent's perception
ability by facilitating communication among multiple agents. However, temporal
asynchrony among agents is inevitable in the real world due to communication
delays, interruptions, and clock misalignments. This issue causes information
mismatch during multi-agent fusion, seriously shaking the foundation of
collaboration. To address this issue, we propose CoBEVFlow, an
asynchrony-robust collaborative perception system based on bird's eye view
(BEV) flow. The key intuition of CoBEVFlow is to compensate motions to align
asynchronous collaboration messages sent by multiple agents. To model the
motion in a scene, we propose BEV flow, which is a collection of the motion
vector corresponding to each spatial location. Based on BEV flow, asynchronous
perceptual features can be reassigned to appropriate positions, mitigating the
impact of asynchrony. CoBEVFlow has two advantages: (i) CoBEVFlow can handle
asynchronous collaboration messages sent at irregular, continuous time stamps
without discretization; and (ii) with BEV flow, CoBEVFlow only transports the
original perceptual features, instead of generating new perceptual features,
avoiding additional noises. To validate CoBEVFlow's efficacy, we create
IRregular V2V(IRV2V), the first synthetic collaborative perception dataset with
various temporal asynchronies that simulate different real-world scenarios.
Extensive experiments conducted on both IRV2V and the real-world dataset
DAIR-V2X show that CoBEVFlow consistently outperforms other baselines and is
robust in extremely asynchronous settings. The code is available at
https://github.com/MediaBrain-SJTU/CoBEVFlow.
