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AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models

文献类型：期刊论文

作者Yang, Zheng1; Zhang, Yuting1; Zeng, Jie1; Yang, Yifan1; Jia, Yufei1; Song, Hua2; Lv, Tiejun3; Sun, Qian4,5; An, Jianping1
刊名DRONES
出版日期2025-05-23
卷号9期号:6页码:57
关键词artificial intelligence large language models safety security unmanned aerial vehicle
DOI10.3390/drones9060392
英文摘要As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities.
资助项目National Key Research and Development Program ; National Natural Science Foundation of China[62371039] ; [308]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001515730600001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/42098]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zeng, Jie; Lv, Tiejun
作者单位1.Beijing Inst Technol, Beijing 100081, Peoples R China
2.China Mobile Res Inst, Beijing 100053, Peoples R China
3.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Zheng,Zhang, Yuting,Zeng, Jie,et al. AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models[J]. DRONES,2025,9(6):57.
APA Yang, Zheng.,Zhang, Yuting.,Zeng, Jie.,Yang, Yifan.,Jia, Yufei.,...&An, Jianping.(2025).AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models.DRONES,9(6),57.
MLA Yang, Zheng,et al."AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models".DRONES 9.6(2025):57.

入库方式： OAI收割

来源：计算技术研究所

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