# HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction

arXiv: https://arxiv.org/abs/2511.10211 · HTML: https://mlsanity.com/paper/2511.10211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving](https://mlsanity.com/paper/2607.19774/) (2026)
- [Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception](https://mlsanity.com/paper/2603.20308/) (2026)
- [HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework](https://mlsanity.com/paper/2607.26283/) (2026)

## Abstract (arXiv)

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.