Active learning-based efficient separation risk assessment in national airspace system

Yi Gao, Yongming Liu, Parikshit Dutta, Oliver Chen, Hari N. Iyer, Bong Jun Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The real-time risk assessment is critical for the decision making and the safety of the National Airspace System (NAS). The adaptive kriging model has the benefit that it can achieve accurate results compared with the sample-based method such as Monte Carlo simulation but with less training data. That is used to develop a framework for the efficient risk assessment (both time-independent and time-dependent problems) in NAS. The framework can be used to the problems with different types of uncertainties and multiple well-defined safety metrics. The efficiency and accuracy of the methods are demonstrated by case studies of risk assessment loss of separation and hazardous weather avoidance.

Original languageEnglish (US)
Title of host publicationAIAA Aviation 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105890
DOIs
StatePublished - 2019
EventAIAA Aviation 2019 Forum - Dallas, United States
Duration: Jun 17 2019Jun 21 2019

Publication series

NameAIAA Aviation 2019 Forum

Conference

ConferenceAIAA Aviation 2019 Forum
CountryUnited States
CityDallas
Period6/17/196/21/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Aerospace Engineering

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