Grant: Alliance Grants
University of Ontario Institute of Technology
$60,000.00
Runway Condition Detection and Analysis using Deep Learning _x000d_
FY 2020-21
Summary
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.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
558582-2020
Name nom
Elgazzar, Khalid
Department département
Electrical, Computer and Software Engineering
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
60000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
703
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Aerospace
Area of application fr
Aérospatial
Research subject code
2720
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Computer systems software
Research subject fr
Logiciel de systèmes informatiques
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
Runway Condition Detection and Analysis using Deep Learning _x000d_
Keyword
NSERC
Application summary
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.
