HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction

• URL: http://arxiv.org/abs/2511.10211v1
• Date: Fri, 14 Nov 2025 01:39:33 GMT
• Title: HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction
• Authors: Yueran Zhao, Zhang Zhang, Chao Sun, Tianze Wang, Chao Yue, Nuoran Li,
• Abstract summary: Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission.<n>Existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents.<n>We propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework.
• Score: 7.171380055232685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents. The former requires effective cross-agent feature alignment to mitigate heterogeneity loss, while the latter renders full-parameter training impractical, highlighting the importance of scalable adaptation. To address these issues, we propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework. We first train a high-performance agent based on heterogeneous graph attention as the foundation for collaborative learning. Then, we design Local Heterogeneous Fine-Tuning and Global Collaborative Fine-Tuning to achieve effective alignment and interaction among heterogeneous agents. The former efficiently extracts modality-specific differences using Hetero-Aware Adapters, while the latter employs the Multi-Cognitive Adapter to enhance cross-agent collaboration and fully exploit the fusion potential. These designs enable substantial performance improvement of the collaborative framework with minimal training cost. We evaluate our approach on the OPV2V-H and DAIR-V2X datasets. Experimental results demonstrate that our method achieves superior perception performance with significantly reduced training overhead, outperforming existing state-of-the-art approaches. Our implementation will be released soon.

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