One Adapter For All: Towards Generalizable Many-To-One Domain Adaptation In Heterogeneous Collaborative Perception

In autonomous driving, multi-agent collaboration enhances individual perception capabilities through information sharing. However, in real-world applications, differences in models and training data among heterogeneous agents inevitably lead to domain gaps between shared features, bringing challenges to collaboration. To address this, existing methods typically rely on a one-to-one adaptation paradigm, necessitating specific training or fine-tuning for every new agent. This results in a large accumulation of adapters on the vehicle, which is unsustainable for resource-constrained edge devices. To overcome these limitations, we propose UniMTO, a Unified adaptation framework that enables Many-To-One adaptation across heterogeneous agents using a single, fixed adapter. Specifically, it treats domain adaptation as a pattern transformation task, disentangling domain-invariant content and domain-specific pattern from features, and achieving source-to-target pattern transformation through recombination based on a Mixture-of-Experts (MoE) mechanism. Extensive experiments on V2V4Real and V2X-Real demonstrate that UniMTO achieves superior generalization, and exhibits strong zero-shot adaptation capabilities, outperforming all state-of-the-art methods, enabling many-to-one domain adaptation with a single, fixed adapter.