城市空中交通作为系统的系统：一种LLM增强的整子方法
Urban Air Mobility as a System of Systems: An LLM-Enhanced Holonic Approach
AI总结：
本文提出一种结合LLM的智能整子架构，解决城市空中交通在动态复杂环境下的系统架构与任务管理挑战，实现去中心化的实时协调与自主适应，为构建更具弹性和以人为中心的UAM生态系统奠定基础。
摘要：
城市空中交通（UAM）是一种新兴的系统的系统（SoS），在系统架构、规划、任务管理和执行方面面临挑战。传统架构方法在动态和复杂环境中的可扩展性、适应性以及无缝资源整合方面存在困难。本文提出了一种结合大型语言模型（LLM）的智能整子架构，以管理UAM的复杂性。整子半自主运行，实现空中出租车、地面交通和垂直起降场之间的实时协调。LLM处理自然语言输入，生成适应性计划，并管理诸如天气变化或空域关闭等中断情况。通过对电动滑板车和空中出租车的多式联运案例研究，我们展示了该架构如何实现动态资源分配、实时重新规划以及无需集中控制的自主适应，从而创建更具弹性和效率的城市交通网络。通过推进去中心化控制和AI驱动的适应性，这项工作为以人为中心、具有弹性的UAM生态系统奠定了基础，未来的工作将致力于混合AI整合和现实世界验证。
英文摘要：
Urban Air Mobility (UAM) is an emerging System of System (SoS) that faces challenges in system architecture, planning, task management, and execution. Traditional architectural approaches struggle with scalability, adaptability, and seamless resource integration within dynamic and complex environments. This paper presents an intelligent holonic architecture that incorporates Large Language Model (LLM) to manage the complexities of UAM. Holons function semi autonomously, allowing for real time coordination among air taxis, ground transport, and vertiports. LLMs process natural language inputs, generate adaptive plans, and manage disruptions such as weather changes or airspace closures.Through a case study of multimodal transportation with electric scooters and air taxis, we demonstrate how this architecture enables dynamic resource allocation, real time replanning, and autonomous adaptation without centralized control, creating more resilient and efficient urban transportation networks. By advancing decentralized control and AI driven adaptability, this work lays the groundwork for resilient, human centric UAM ecosystems, with future efforts targeting hybrid AI integration and real world validation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
