# Cross-Modal Semantic Communication for Heterogeneous Collaborative Perception

arXiv: https://arxiv.org/abs/2511.20000 · HTML: https://mlsanity.com/paper/2511.20000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Domain-Generalized Adaptive Semantic Communication for Collaborative Perception](https://mlsanity.com/paper/2608.00056/) (2026)

## Abstract (arXiv)

Collaborative perception, an emerging paradigm in autonomous driving, has been introduced to mitigate the limitations of single-vehicle systems, such as limited sensor range and occlusion. To improve the robustness of inter-vehicle data sharing, semantic communication has recently further been integrated into collaborative perception systems to enhance overall performance. However, practical deployment of such systems is challenged by the heterogeneity of sensors across different connected autonomous vehicles (CAVs). This diversity in perceptual data complicates the design of a unified communication framework and impedes the effective fusion of shared information. To address this challenge, we propose a novel cross-modal semantic communication (CMSC) framework to facilitate effective collaboration among CAVs with disparate sensor configurations. Specifically, the framework first transforms heterogeneous perceptual features from different sensor modalities into a unified and standardized semantic space. Subsequently, encoding, transmission, and decoding are performed within this semantic space, enabling seamless and effective information fusion. Extensive experiments demonstrate that CMSC achieves significantly stronger perception performance than existing methods, particularly in low signal-to-noise ratio (SNR) regimes.