What if an unstable aircraft approach could be identified early enough for a flight instructor to intervene?
What if an unstable aircraft approach could be identified early enough for a flight instructor to intervene? That is the problem behind this open source project: AI Early Warning for Unstable Approaches in GA Flight Training. The project uses public ADS-B data from the OpenSky Network around Daytona Beach International Airport (KDAB) to analyze general aviation training approaches. It currently:…
A new open source project aims to identify unstable aircraft approaches early enough for flight instructors to intervene. Utilizing public ADS-B data from the OpenSky Network around Daytona Beach International Airport, the AI Early Warning system analyzes general aviation training approaches. The system extracts individual approaches from flight tracks, filtering out unreliable ADS-B reports and focusing on common training aircraft.
It detects potential instability using sink rate and speed rules, and creates a blind review sheet for flight instructors. The system compares automated flags against instructor judgment. In the initial dataset, covering 3 hours of flight activity, 72 approaches were selected for instructor review and one Cessna 172S approach triggered both speed and sink-rate warnings.
The project emphasizes that ground speed is affected by wind and ADS-B measurements have limitations, so instructor review remains crucial. The next step is to validate and calibrate the rules against expert human judgment. This project showcases how open source aviation data can turn into a practical safety research problem.
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