Privacy-Preserving V2X Collaborative Perception Integrating Unknown Collaborators

Abstract

Vehicle-to-everything (V2X) collaborative perception has recently gained increasing attention in autonomous driving due to its ability to enhance scene understanding by integrating information from other collaborators, e.g. vehicles or infrastructure. Existing algorithms usually share deep features to achieve a trade-off between accuracy and bandwidth. However, most of these methods require joint training of all agents, which results in privacy leakage and is impractical and unacceptable in the real world. Sharing prediction results seems to be a direct solution, but its performance is suboptimal and sensitive to localization noise and communication delay. In this paper, we propose a privacy-preserving collaborative perception framework, where each agent is separately trained with its own dataset and the ego vehicle needs to integrate with completely unknown collaborators. Specifically, we propose MSD, a multi-scale feature fusion method combined with deformable attention, to better fuse features of different agents. We also propose a plug-in domain adapter to align the features from unknown collaborators to ego-domain. Extensive experiments on the challenging DAIR-V2X and V2V4Real demonstrate that: 1) MSD achieves remarkable performance, outperforming others by at least 2.8% and 6.7% in AP0.7 on DAIR-V2X and V2V4Real, respectively; 2) After domain adaptation, it significantly outperforms the No Fusion, Late Fusion scenarios and can approach or even surpass the performance of joint training. We truly achieves privacy-preserving collaboration, providing a new paradigm for the study of collaborative perception, which is crucial for practical applications.