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Advancing Probabilistic Machine Learning to Deliver Safer, More Efficient, and Predictable Air Traffic Control

Advancing Probabilistic Machine Learning to Deliver Safer, More Efficient, and Predictable Air Traffic Control
推进概率机器学习以提供更安全、更高效和可预测的空中交通管制
批准号:
EP/V056522/1
负责人:
Richard Everson
金额:
$402.23万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

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中文摘要
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英文摘要
The ambition of this partnership between NATS and The Alan Turing Institute is to develop the fundamental science to deliver the world's first AI system to control a section of airspace in live trials.Our research will take a hierarchical approach to air traffic control (ATC) by developing a digital twin alongside a multi-agent machine-learning control system for UK airspace. Furthermore, the partnership will develop technical approaches to deploy trustworthy AI systems, considering how safety, explainability and ethics are embedded within our methods, so that we can deliver new tools which work in harmony with human air traffic controllers in a safety-critical environment.Little has changed in the fundamental infrastructure of UK airspace in the past 50 years, but demand for aviation has increased a hundredfold. Aviation 2050, a recent government green paper, underlines the importance of the aviation network to the prosperity of the UK to the value of £22 billion annually. Yet our nation is at risk without rapid action to modernise our airspace and control methods, to ensure they can handle a future increase in UK passenger traffic of over 50% by 2050 and new challenges arising from unmanned aircraft, both against a backdrop of increasing global pressures to transform the sector's environmental impact. The augmentation of live air traffic control through the use of AI agents which can handle the complexity and uncertainties in the system has transformative potential for NATS's business. This will positively impact live operations, as well as a research tool and training facility for new ATCOs. Correspondingly, NATS's research vision is to exploit new approaches to AI that enable increases in safety, capacity and environmental sustainability while streamlining air traffic controller training.The anticipated benefits of AI systems to air traffic control have come at a critical time, providing us with an opportunity to respond effectively to the unprecedented challenges which arise from a triad of crises: the coronavirus 2019 (Covid-19) pandemic, Brexit and global warming. The UK must develop independent technical advances in the sector, without compromising sustainability targets.The Alan Turing Institute is positioned at the rapidly evolving frontiers of probabilistic machine learning, safe and trustworthy AI and reproducible software engineering. Matching this with the world-leading expertise of NATS, supported by a world-first data set of more than 20 million flight records, means that this partnership is in a unique position to build the first multi AI agents system to deliver tactical control of UK airspace.
期刊论文(6)
专著(0)
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会议论文
DOI: 10.1098/rspa.2022.0607
发表时间: 2022-10
期刊: Proceedings of the Royal Society A
影响因子: --
作者: [Nick Pepper;Marc Thomas;George De Ath;Enrico Oliver;R. Cannon;R. Everson;T. Dodwell]
通讯作者: Nick Pepper;Marc Thomas;George De Ath;Enrico Oliver;R. Cannon;R. Everson;T. Dodwell
DOI: 10.5555/3535850.3535999
发表时间: 2022-02
期刊: ArXiv
影响因子: --
作者: [Luca Viano;Yu-ting Huang;Parameswaran Kamalaruban;Craig Innes;S. Ramamoorthy;Adrian Weller]
通讯作者: Luca Viano;Yu-ting Huang;Parameswaran Kamalaruban;Craig Innes;S. Ramamoorthy;Adrian Weller
DOI: 10.48550/arxiv.2304.06701
发表时间: 2023-04
期刊: ArXiv
影响因子: --
作者: [Umang Bhatt;Valerie Chen;Katherine M. Collins;Parameswaran Kamalaruban;Emma Kallina;Adrian Weller;Ameet Talwalkar]
通讯作者: Umang Bhatt;Valerie Chen;Katherine M. Collins;Parameswaran Kamalaruban;Emma Kallina;Adrian Weller;Ameet Talwalkar
Lagrangian Manifold Monte Carlo on Monge Patches
Monge 补丁上的拉格朗日流形蒙特卡罗
DOI: 10.17863/cam.81980
发表时间: 2022
期刊:
影响因子: --
作者: [Hartmann M]
通讯作者: Hartmann M
Data-Driven Surrogate-Assisted Evolutionary Fluid Dynamic Optimisation
  • 批准号:
    EP/M017915/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $70.67万
  • 财政年份:
    2015
  • 负责人:
    Richard Everson
  • 依托单位:
海外基金