  Cooperatively utilizing both ego-vehicle and infrastructure sensor data can
significantly enhance autonomous driving perception abilities. However, the
uncertain temporal asynchrony and limited communication conditions can lead to
fusion misalignment and constrain the exploitation of infrastructure data. To
address these issues in vehicle-infrastructure cooperative 3D (VIC3D) object
detection, we propose the Feature Flow Net (FFNet), a novel cooperative
detection framework. FFNet is a flow-based feature fusion framework that uses a
feature flow prediction module to predict future features and compensate for
asynchrony. Instead of transmitting feature maps extracted from still-images,
FFNet transmits feature flow, leveraging the temporal coherence of sequential
infrastructure frames. Furthermore, we introduce a self-supervised training
approach that enables FFNet to generate feature flow with feature prediction
ability from raw infrastructure sequences. Experimental results demonstrate
that our proposed method outperforms existing cooperative detection methods
while only requiring about 1/100 of the transmission cost of raw data and
covers all latency in one model on the DAIR-V2X dataset. The code is available
at
\href{https://github.com/haibao-yu/FFNet-VIC3D}{https://github.com/haibao-yu/FFNet-VIC3D}.
