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.

翻译：协同感知近年来在自动驾驶领域备受关注，通过允许车辆间交换额外信息来提升感知质量。然而，由于网联自动驾驶车辆（CAVs）面临多样化的环境条件与数据异质性，部署协同感知系统可能导致域偏移问题。为应对这些挑战，我们提出一种统一的域泛化框架，可同时应用于协同感知的训练与推理阶段。在训练阶段，我们引入振幅增强（AmpAug）方法，通过增强低频图像变化来扩展模型跨域学习能力；同时采用元一致性训练策略模拟域偏移，利用精心设计的一致性损失函数优化模型以鼓励域不变表征。在推理阶段，我们提出系统内域对齐机制，在推理前减少或消除CAVs间的域差异。综合实验验证了所提方法相较于现有前沿工作的有效性。代码将发布于 https://github.com/DG-CAVs/DG-CoPerception.git。