{"title":"Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace","authors":"Ashim Kumar Thapa, J. Shortle, L. Sherry","doi":"10.1109/ICNS58246.2023.10124323","DOIUrl":null,"url":null,"abstract":"Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and \"total\" risk for a given Air Traffic concept-of-operation (e.g. parallel approaches). The fidelity of the models, and assumptions used in the models, are determined by the required confidence interval required for the safety analysis, the capabilities of current analytical and simulation methods, availability of empirical data sets, and the capabilities of computational resources.This paper provides an overview of the state-of-the-art for CRMs for Terminal Area operations. Opportunities to apply recently developed AI/ML, and data analytics methods such as analytical and rare-event simulation methods, availability of empirical data sets, and leverage available computational resources are identified.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNS58246.2023.10124323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and "total" risk for a given Air Traffic concept-of-operation (e.g. parallel approaches). The fidelity of the models, and assumptions used in the models, are determined by the required confidence interval required for the safety analysis, the capabilities of current analytical and simulation methods, availability of empirical data sets, and the capabilities of computational resources.This paper provides an overview of the state-of-the-art for CRMs for Terminal Area operations. Opportunities to apply recently developed AI/ML, and data analytics methods such as analytical and rare-event simulation methods, availability of empirical data sets, and leverage available computational resources are identified.