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[2506.02649] From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV
Summary
This article explores the integration of Large Language Models (LLMs) with Uncrewed Aerial Vehicles (UAVs) for enhanced public safety, focusing on In-Context Learning (ICL) to improve decision-making during emergencies.
Why It Matters
The research highlights the potential of LLMs to enhance UAV capabilities in critical public safety scenarios, addressing limitations of traditional methods. This integration can lead to more efficient emergency responses, improved situational awareness, and better resource management.
Key Takeaways
- LLMs can significantly improve UAV navigation and control in emergency situations.
- In-Context Learning allows UAVs to adapt to new tasks without retraining.
- Deploying LLMs at the network edge enhances real-time decision-making and data privacy.
- The proposed framework reduces packet loss in data collection compared to conventional methods.
- Future research directions include optimizing LLMs for better performance in public safety applications.
Computer Science > Artificial Intelligence arXiv:2506.02649 (cs) [Submitted on 3 Jun 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV Authors:Yousef Emami, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida, Zhu Han View a PDF of the paper titled From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV, by Yousef Emami and 5 other authors View PDF HTML (experimental) Abstract:A public safety Uncrewed Aerial Vehicle (UAV) enhances situational awareness during emergency response. Its agility, mobility optimization, and ability to establish Line-of-Sight (LoS) communication make it increasingly important for managing emergencies such as disaster response, search and rescue, and wildfire monitoring. Although Deep Reinforcement Learning (DRL) has been used to optimize UAV navigation and control, its high training complexity, low sample efficiency, and the simulation-to-reality gap limit its practicality in public safety applications. Recent advances in Large Language Models (LLMs) present a promising alternative. With strong reasoning and generalization abilities, LLMs can adapt to new tasks through In-Context Learning (ICL), enabling task adaptation via natural language prompts and example-based guidance without retraining. Deploying LLMs at the network edge, rather than in the cloud, further reduces latency ...
