Artificial Intelligence controller able to manage Air traffic Control (ATC) and Air Traffic Flow Management (ATFM) within a single framework

人工智能控制器能够在单一框架内管理空中交通管制（ATC）和空中交通流量管理（ATFM）

• 批准号: 10075687
• 金额: $ 24.12万
• 依托单位: UNIVERSITY OF WARWICK
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10075687
• 关键词: Artificial; Intelligence; controller; able; manage

Air Traffic Flow Management (ATFM) is the problem of adjusting the traffic demand in each traffic volume using ATFM measures so that aircraft can be safely separated during the subsequent Air Traffic Control (ATC) process. On the other hand, ATC officers (ATCOs) give different aircraft heading, speed, and flight level change instructions to separate them in flight. Both ATFM and ATC problems have been subject of research during decades, however, all previous works addressed the ATFM and ATC problems independently. The project aims to develop an HyperSolver based on advanced Artificial Intelligent Reinforcement Learning method with continuous reassessment and dynamic updates, i.e. an holistic solver from end-to-end, covering the whole process to manage, density of aircraft, complexity of trajectories, interactions (potential conflict in Dynamic Capacity Balancing timeframe) of trajectories, conflict of trajectories at medium term and conflict of trajectories at short-term.

空中交通流量管理（ATFM）是指在空中交通管制（ATC）过程中，通过ATFM措施调整各交通量的交通需求，使飞机能够安全地分离。另一方面，空中交通管制人员（ATCO）会发出不同的飞机航向、速度和飞行高度层改变指令，以将它们在飞行中分开。这两个ATFM和ATC的问题已经研究了几十年，然而，所有以前的作品解决ATFM和ATC问题独立。该项目旨在开发一个基于先进的人工智能强化学习方法的HyperSolver，该方法具有持续的重新评估和动态更新，即从端到端的整体求解器，涵盖整个管理过程，飞机密度，轨迹复杂性，交互（动态容量平衡时间框架中的潜在冲突）轨迹，中期的轨迹冲突和短期的轨迹冲突。