Review of Current State of Artificial Intelligence/Machine Learning and Other Advanced Techniques Related to Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace

693KA9-20-D-00004DTFACT-14-D-00004Collision 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 CRMs for terminal area operations. Opportunities to apply recently developed artificial intelligence/machine learning (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