  Cooperatively utilizing both ego-vehicle and infrastructure sensor data can
significantly enhance autonomous driving perception abilities. However,
temporal asynchrony and limited wireless communication in traffic environments
can lead to fusion misalignment and impact detection performance. This paper
proposes Feature Flow Net (FFNet), a novel cooperative detection framework that
uses a feature flow prediction module to address these issues in
vehicle-infrastructure cooperative 3D object detection. Rather than
transmitting feature maps extracted from still-images, FFNet transmits feature
flow, which leverages the temporal coherence of sequential infrastructure
frames to predict future features and compensate for asynchrony. Additionally,
we introduce a self-supervised approach to enable FFNet to generate feature
flow with feature prediction ability. Experimental results demonstrate that our
proposed method outperforms existing cooperative detection methods while
requiring no more than 1/10 transmission cost of raw data on the DAIR-V2X
dataset when temporal asynchrony exceeds 200$ms$. The code is available at
\href{https://github.com/haibao-yu/FFNet-VIC3D}{https://github.com/haibao-yu/FFNet-VIC3D}.
