The rapid development of the low-altitude economy emphasizes the critical need for effective perception and intent recognition of non-cooperative unmanned aerial vehicles (UAVs). The advanced generative reasoning capabilities of multimodal large language models (MLLMs) present a promising approach in such tasks. In this paper, we focus on the combination of UAV intent recognition and the MLLMs. Specifically, we first present an MLLM-enabled UAV intent recognition architecture, where the multimodal perception system is utilized to obtain real-time payload and motion information of UAVs, generating structured input information, and MLLM outputs intent recognition results by incorporating environmental information, prior knowledge, and tactical preferences. Subsequently, we review the related work and demonstrate their progress within the proposed architecture. Then, a use case for low-altitude confrontation is conducted to demonstrate the feasibility of our architecture and offer valuable insights for practical system design. Finally, the future challenges are discussed, followed by corresponding strategic recommendations for further applications.
@article{arxiv.2509.06312,
title = {Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition},
author = {Guangyu Lei and Tianhao Liang and Yuqi Ping and Xinglin Chen and Longyu Zhou and Junwei Wu and Xiyuan Zhang and Huahao Ding and Xingjian Zhang and Weijie Yuan and Tingting Zhang and Qinyu Zhang},
journal= {arXiv preprint arXiv:2509.06312},
year = {2025}
}
Comments
The paper has been submitted to IEEE Internet of Things Magazine