  Collaborative perception has recently gained significant attention in
autonomous driving, improving perception quality by enabling the exchange of
additional information among vehicles. However, deploying collaborative
perception systems can lead to domain shifts due to diverse environmental
conditions and data heterogeneity among connected and autonomous vehicles
(CAVs). To address these challenges, we propose a unified domain generalization
framework applicable in both training and inference stages of collaborative
perception. In the training phase, we introduce an Amplitude Augmentation
(AmpAug) method to augment low-frequency image variations, broadening the
model's ability to learn across various domains. We also employ a
meta-consistency training scheme to simulate domain shifts, optimizing the
model with a carefully designed consistency loss to encourage domain-invariant
representations. In the inference phase, we introduce an intra-system domain
alignment mechanism to reduce or potentially eliminate the domain discrepancy
among CAVs prior to inference. Comprehensive experiments substantiate the
effectiveness of our method in comparison with the existing state-of-the-art
works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git.
