Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition

• URL: http://arxiv.org/abs/2509.06312v1
• Date: Mon, 08 Sep 2025 03:34:56 GMT
• Title: Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition
• Authors: Guangyu Lei, Tianhao Liang, Yuqi Ping, Xinglin Chen, Longyu Zhou, Junwei Wu, Xiyuan Zhang, Huahao Ding, Xingjian Zhang, Weijie Yuan, Tingting Zhang, Qinyu Zhang,
• Abstract summary: The rapid development of the low-altitude economy emphasizes the need for effective perception and intent recognition of non-cooperative unmanned aerial vehicles (UAVs)<n>The advanced generative reasoning capabilities of multimodal large language models (MLLMs) present a promising approach in such tasks.<n>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.
• Score: 27.668388138106312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial [24.92730472637731]
  Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility.<n>Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence.
  arXiv  Detail & Related papers  (2026-02-23T05:56:43Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
  We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
  arXiv  Detail & Related papers  (2025-11-26T04:44:27Z)
• A Survey on Agentic Multimodal Large Language Models [84.18778056010629]
  We present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs)<n>We explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents.<n>To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs.
  arXiv  Detail & Related papers  (2025-10-13T04:07:01Z)
• A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives [65.3369988566853]
  Recent studies have demonstrated that adversaries can replicate a target model's functionality.<n>Model Extraction Attacks pose threats to intellectual property, privacy, and system security.<n>We propose a novel taxonomy that classifies MEAs according to attack mechanisms, defense approaches, and computing environments.
  arXiv  Detail & Related papers  (2025-08-20T19:49:59Z)
• Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO [63.140883026848286]
  Active vision refers to the process of actively selecting where and how to look in order to gather task-relevant information.<n>Recently, the use of Multimodal Large Language Models (MLLMs) as central planning and decision-making modules in robotic systems has gained extensive attention.
  arXiv  Detail & Related papers  (2025-05-27T17:29:31Z)
• Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models [51.84752285423123]
  We introduce a novel metric, $Rank_e$, to quantify the effect of prior knowledge of the vision encoder on MLLM performance.<n>We propose VisPRE (Vision Prior Remediation), a two-stage training framework that explicitly incorporates prior knowledge at the vision encoder level.<n> Experimental results demonstrate that augmenting vision encoder's prior knowledge substantially boosts the visual understanding capabilities of MLLMs.
  arXiv  Detail & Related papers  (2025-03-23T11:33:09Z)
• UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility [33.73170899086857]
  Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains.<n>This paper explores the integration of large language models (LLMs) and UAVs.<n>It categorizes and analyzes key tasks and application scenarios where UAVs and LLMs converge.
  arXiv  Detail & Related papers  (2025-01-04T17:32:12Z)
• Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach [0.3495246564946556]
  This study explores the application of Large Language Models in UAV control. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols.
  arXiv  Detail & Related papers  (2024-10-23T06:56:53Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Large Language Models for UAVs: Current State and Pathways to the Future [6.85423435360359]
  Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors. This work explores the significant potential of integrating UAVs and Large Language Models (LLMs) to propel the development of autonomous systems.
  arXiv  Detail & Related papers  (2024-05-02T21:30:10Z)
• Cognitive Planning for Object Goal Navigation using Generative AI Models [0.979851640406258]
  We present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach enables a robot to navigate unfamiliar environments by leveraging Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
  arXiv  Detail & Related papers  (2024-03-30T10:54:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.