Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

TL;DR

This paper proposes a hierarchical system architecture and key techniques for deploying large AI models in low-altitude economy services, addressing resource constraints and environmental mismatches to enable scalable, adaptive aerial applications.

Contribution

It introduces a novel hierarchical framework and enabling techniques for effective LAIM deployment in LAE, bridging resource gaps and environmental challenges.

Findings

Validated through real-world case studies.

Demonstrated scalable and adaptive service delivery.

Outlined open challenges for future research.

Abstract

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.