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Cooperatively utilizing both ego-vehicle and infrastructure sensor data cansignificantly enhance autonomous driving perception abilities. However, theuncertain temporal asynchrony and limited communication conditions can lead tofusion misalignment and constrain the exploitation of infrastructure data. Toaddress these issues in vehicle-infrastructure cooperative 3D (VIC3D) objectdetection, we propose the Feature Flow Net (FFNet), a novel cooperativedetection framework. FFNet is a flow-based feature fusion framework that uses afeature flow prediction module to predict future features and compensate forasynchrony. Instead of transmitting feature maps extracted from still-images,FFNet transmits feature flow, leveraging the temporal coherence of sequentialinfrastructure frames. Furthermore, we introduce a self-supervised trainingapproach that enables FFNet to generate feature flow with feature predictionability from raw infrastructure sequences. Experimental results demonstratethat our proposed method outperforms existing cooperative detection methodswhile only requiring about 1/100 of the transmission cost of raw data andcovers all latency in one model on the DAIR-V2X dataset. The code is availableat\href{https://github.com/haibao-yu/FFNet-VIC3D}{https://github.com/haibao-yu/FFNet-VIC3D}.

Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection