Using Deep Learning to Predict Unstable Approaches for General Aviation Aircraft
Author:
Affiliation:
1. Stanford University, Stanford, California 94305
2. Embry-Riddle Aeronautical University, Daytona Beach, Florida 32114
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering
Link
https://arc.aiaa.org/doi/pdf/10.2514/1.I011132
Reference12 articles.
1. Implementation Plan 2018-19, N., Aircraft Owners and Pilots Association 30th Joseph T. Nall Report, Frederick, MD, 2018, https://www.aopa.org/training-and-safety/air-safety-institute/accident-analysis/joseph-t-nall-report/nall-report-figure-view?category=all&year=2020&condition=all&report=true.
2. A comparison of general aviation accidents involving airline pilots and instrument-rated private pilots
3. Occupant Injury Severity in General Aviation Accidents Involving Excessive Landing Airspeed
4. Applying Machine Learning to Enhance Runway Safety Through Runway Excursion Risk Mitigation
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