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Runway Condition Detection and Analysis using Deep Learning

Runway Condition Detection and Analysis using Deep Learning
使用深度学习进行跑道状况检测和分析
批准号:
558582-2020
负责人:
Elgazzar, Khalid
金额:
$4.37万
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

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中文摘要
翻译
航空业的基本概念是“起飞是可有可无的,降落是必须的”。最近的研究表明,大多数致命的航空事故发生在飞机着陆期间,由于飞行员相关的失控错误。由于恶劣的天气条件,特别是对于经验不足的飞行员来说,在受污染的跑道上,风险变得更高。目前有几种方法可以测量跑道的摩擦力,但没有一种是实时的,也不能提供准确的不安全着陆情况。该项目将开发一个实时分析平台,以支持实时决策并向利益相关者发出警报。人工智能算法将处理从ADS-B收集的数据,并确定飞机在着陆期间是否可能在跑道上经历了异常减速和/或方向符合性。分析结果将使用图形用户界面进行可视化,该图形用户界面在视觉上协调飞机着陆位置、时间和天气数据,以容易地说明飞机从中心线的高度、地面速度、减速度和位移,从着陆前500'到跑道偏移或在铺开后退出。协调后的数据将以一种格式存储,除其他外,这种格式允许轻松挖掘数据,以监测趋势,例如随着时间的推移,类似飞机在潮湿跑道上的停止距离延长,表明橡胶积聚或纹理损失,或跑道表面不规则的发展,如所谓的“波音颠簸”。这一接口将为机场当局和其他相关利益攸关方提供一个理想的平台,以做出明智的决定,并向飞机提供安全着陆指令。 该项目将帮助主要合作伙伴Team Eagle构建一个最小可行产品(MVP),用于向最初的目标客户群进行商业化,并展示所开发技术的可行性和可用性。这些成果将支持Team Eagle为航空业带来急需的新技术,确保早期市场采用。
英文摘要
The aviation industry adopts a primary concept that "while takeoff is optional, landing is mandatory". Recent studies have shown that the majority of fatal aviation accidents happen during aircraft landing due to pilot-related errors of loss of control. The risk becomes significant higher on contaminated runways due to inclement weather conditions, especially for less experienced pilots. There are currently several methods that can measure the friction of runways, but none of them is real-time or can provide an accurate profile of unsafe landing. This project will develop a real-time analytics platform to support real-time decision making and issue alerts to the stakeholders. AI algorithms will process collected data from ADS-B and determine whether an aircraft has possibly experienced anomalous decelerations on the runway and/or directional compliance during landing. The analytical results will be visualized using graphical user interface that visually reconciles aircraft landing position, time and weather data to readily illustrate altitude, ground speed, deceleration and displacement of the aircraft from centerline, from 500' before touchdown to runway excursion or exit after roll out. The reconciled data will be stored in a format that allows, among other things, easy mining of the data to monitor trends like lengthening stopping distances of similar aircraft over time on wet runways indicating rubber build up or loss of texture, or the development of runway surface irregularities like what has been called 'the Boeing bump'. Such an interface will provide an ideal platform for airport authorities and other concerned stakeholders to make informed decisions and provide safe landing directives to aircrafts. The project will help Team Eagle, the key partner, build a Minimal Viable Product (MVP) for commercialization to the initial target customer base; and demonstrate feasibility and usability of the developed technology. The outcomes will support Team Eagle to bring a new much needed technology to the aviation industry secure early market adoption.
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