Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition
This addresses security concerns for low-altitude airspace management, but appears incremental as it applies existing MLLM capabilities to a specific domain.
The paper tackles the problem of recognizing non-cooperative UAV intents in low-altitude airspace by proposing an MLLM-enabled architecture that integrates multimodal perception with generative reasoning, demonstrating feasibility through a use case in low-altitude confrontation.
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.