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 200ms. The code is available at \href{https://github.com/haibao-yu/FFNet-VIC3D}{https://github.com/haibao-yu/FFNet-VIC3D}.
@article{arxiv.2303.10552,
title = {Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction},
author = {Haibao Yu and Yingjuan Tang and Enze Xie and Jilei Mao and Jirui Yuan and Ping Luo and Zaiqing Nie},
journal= {arXiv preprint arXiv:2303.10552},
year = {2023}
}