By facilitating communication among multiple agents, collaborative perception
can substantially boost each agent's perception ability. However, temporal
asynchrony among agents is inevitable in 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 3D 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 will be released.

通过鸟瞰流（BEV flow）对异步协同感知消息进行补偿，CoBEVFlow 提出了一种异步鲁棒的基于 3D 视觉感知系统，能够处理不规则、连续时间戳的协同感知消息，而不需要离散化，并且只传输原始感知特征而不引入额外噪音，通过在 IRV2V 和 DAIR-V2X 数据集上的大量实验证明了 CoBEVFlow 的有效性和对极端异步情况的鲁棒性。