PT Journal
AU Debora Gil
   Aura Hernandez-Sabate
   Julien Enconniere
   Saryani Asmayawati
   Pau Folch
   Juan Borrego-Carazo
   Miquel Angel Piera
TI E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights
SO IEEE Access
JI ACCESS
PY 2022
BP 7489
EP 7503
VL 10
DI 10.1109/ACCESS.2021.3138167
AB More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.
ER