用大模型提升低空经济智能水平，解决算力与环境适配难题
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
- 构建分层系统架构，支持大模型在低空设备上部署
- 提出任务导向执行流程，实现服务的可扩展与自适应
- 针对真实场景验证框架有效性，适合低空智联网研究者
低空经济（LAE）代表一种新兴的商业与社会空中活动范式。大型人工智能模型（LAIMs）为提升LAE服务智能化提供了变革性潜力。然而，在LAE中部署LAIMs面临多重挑战：模型的计算/存储需求与低空实体有限的机载资源之间存在显著差距；实验室训练的LAIMs与动态物理环境不匹配；传统感知、通信与计算分离设计效率低下。为此，我们首先提出专为LAIM部署设计的分层系统架构，并展示典型LAE应用场景。接着，探索促进LAIMs与低空系统协同演进的关键技术，引入面向任务的执行流水线，实现可扩展、自适应的服务交付。随后通过真实世界案例验证所提框架。最后，指出开放挑战以激发未来研究。
原文摘要 · Abstract (English)
Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer transformative potential to further enhance the intelligence of LAE services. However, deploying LAIMs in LAE poses several challenges, including the significant gap between their computational/storage demands and the limited onboard resources of LAE entities, the mismatch between lab-trained LAIMs and dynamic physical environments, and the inefficiencies of traditional decoupled designs for sensing, communication, and computation. To address these issues, we first propose a hierarchical system architecture tailored for LAIM deployment and present representative LAE application scenarios. Next, we explore key enabling techniques that facilitate the mutual co-evolution of LAIMs and low-altitude systems, and introduce a task-oriented execution pipeline for scalable and adaptive service delivery. Then, the proposed framework is validated through real-world case studies. Finally, we outline open challenges to inspire future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。