Towards Secure Low-Altitude Airspace: MLLM-Enabled UAV Intent Recognition

With the rapid growth of low-altitude technologies, safety risks are rising, particularly from security threats posed by non-cooperative unmanned aerial vehicles (UAVs), which makes accurate perception and intent recognition of aerial targets a central requirement for building a secure low-altitude environment. 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 the 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.