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 and preserves data privacy, making them suitable for real-time, mission-critical public safety UAVs. This paper proposes integrating LLM-assisted ICL with public safety UAVs to address key functions such as path planning and velocity control in emergency response. We present a case study on data collection scheduling, demonstrating that the LLM-assisted ICL framework can significantly reduce packet loss compared to conventional approaches while also mitigating potential jailbreaking vulnerabilities. Finally, we discuss LLM optimizers and outline future research directions. The ICL framework enables adaptive, context-aware decision-making for public safety UAVs, offering a lightweight and efficient solution to enhance UAV autonomy and responsiveness in emergencies.

翻译：公共安全无人机在应急响应中能显著提升态势感知能力。其敏捷性、移动性优化以及建立视距通信的能力，使其在灾害响应、搜救行动和野火监测等应急管理中日益重要。尽管深度强化学习已被用于优化无人机导航与控制，但其高训练复杂度、低样本效率以及仿真与现实的差距限制了其在公共安全应用中的实用性。大语言模型的最新进展提供了一种有前景的替代方案。凭借强大的推理和泛化能力，大语言模型能够通过上下文学习适应新任务，仅需自然语言提示和示例引导即可实现任务适配，无需重新训练。将大语言模型部署在网络边缘而非云端，进一步降低了延迟并保障了数据隐私，使其适用于实时、任务关键的公共安全无人机。本文提出将大语言模型辅助的上下文学习与公共安全无人机相结合，以解决应急响应中的路径规划和速度控制等关键功能。我们通过数据收集调度的案例研究，证明相较于传统方法，大语言模型辅助的上下文学习框架能显著降低数据包丢失率，同时缓解潜在的越狱漏洞风险。最后，我们探讨了大语言模型优化器并展望了未来研究方向。该上下文学习框架为公共安全无人机实现了自适应、情境感知的决策能力，为提升无人机在紧急情况下的自主性和响应能力提供了一种轻量高效的解决方案。