Papers9月10日 09:27

多模态感知增强V2X网络波束预测

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

arXiv cs.LG@Chen Shang et al. (4)

Paper

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Why we picked it

清华团队用多模态传感器解决了车联网波束预测难题，比传统射频方法更可靠。

研究人员提出BeamTransF框架，整合摄像头、激光雷达、雷达和GPS数据提升车联网波束预测准确性。该模型在真实多模态V2X数据集上表现优于基准方法。

Body

生成模块可在传感器数据缺失时重建特征，提高系统鲁棒性。实验证明该框架在复杂车辆环境中可靠性更高。

Original · arXiv cs.LG

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include V2X, BeamTransF, 多模态感知, 车联网.

Source attribution: curated from arXiv cs.LG (@Chen Shang, Dinh Thai Hoang, Diep N. Nguyen, Jiadong Yu).

#V2X #BeamTransF #多模态感知 #车联网 #波束预测

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