TY - JOUR
T1 - Machine learning and cognitive ergonomics in air traffic management
T2 - Recent developments and considerations for certification
AU - Kistan, Trevor
AU - Gardi, Alessandro
AU - Sabatini, Roberto
N1 - Publisher Copyright:
© 2018 by the authors.
PY - 2018
Y1 - 2018
N2 - Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human-machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.
AB - Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human-machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.
KW - Air traffic management
KW - Cognitive HMI
KW - Computer-aided verification
KW - Explainable artificial intelligence
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85062152075&partnerID=8YFLogxK
U2 - 10.3390/aerospace5040103
DO - 10.3390/aerospace5040103
M3 - Article
AN - SCOPUS:85062152075
SN - 2226-4310
VL - 5
JO - Aerospace
JF - Aerospace
IS - 4
M1 - 103
ER -