Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

2025-05-28

AI Summary

To address three critical challenges in deploying Large AI Models (LAIMs) for low-altitude economy applications—severe onboard resource constraints, model-environment misalignment under dynamic physical conditions, and fragmentation among perception, communication, and computation—this paper proposes a hierarchical LAIM collaborative deployment framework. Methodologically, it establishes a co-evolution mechanism between low-altitude systems and LAIMs, designs a task-oriented elastic execution pipeline to enable deep integration of perception-communication-computation scheduling, and incorporates model lightweight adaptation, environment-aware inference, and layered architecture design to improve resource efficiency and environmental robustness. Experimental validation in realistic low-altitude scenarios demonstrates that the framework significantly enhances service responsiveness (≥35% reduction in latency) and adaptability to dynamic environments. It thus provides a scalable, highly reliable paradigm for empowering low-altitude intelligent agents with LAIMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Bridging computational demands of LAIMs with limited LAE onboard resources
• Adapting lab-trained LAIMs to dynamic physical environments
• Integrating sensing, communication, and computation for efficient LAE services

Innovation

• Hierarchical system architecture for LAIM deployment
• Task-oriented execution pipeline for adaptive services
• Mutual co-evolution of LAIMs and low-altitude systems