Technology Category
Machine Learning: Large Multimodal Models (LMMs)Planning, Routing, and Scheduling: Planning with Language ModelsHumans and AI: Human-Aware Planning and Behavior Prediction
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Hierarchical system architecture for LAIM deployment
Task-oriented execution pipeline for adaptive services
Mutual co-evolution of LAIMs and low-altitude systems
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