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

Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving