Enhancing Scenario-Centric Air Traffic Control Training

As one of the main focuses of Federal Aviation Administration (FAA) Academy, Air Traffic Control (ATC) training program heavily relies on simulation-based training and is constantly looking into optimizing the use of such technologies. On the other hand, ATC simulation scenarios are not only used for training purposes, but also are key components for conducting human performance experiments. From a recent visit at the FAA Academy, it is well apparent that there is an immediate need for a diverse pool of ATC training scenarios available to trainees off-site. Currently, training scenarios are generated manually from a subject-matter-expert’s (i.e. controllers) oral or written briefing. The effort in extracting and verifying operational scenarios and translating them into a machine-understandable language is rather cumbersome and currently conducted completely manual. To address these challenges, here we propose to develop a scenario exploration technology that provides a platform for FAA Academy trainees and instructors to exercise variety of scenarios in the ATC domain. The proposed technology provides a platform for instructors and trainees to explore various training exercises. By taking a model-driven approach and extending the recently proposed domain-specific Aviation Scenario Definition Language (ASDL), we provide ATC scenario specification and exploration platform to easily create a variety of ATC scenarios. Similar to ASDL approach, we first define an ontology for ATC extension, then specify scenario logic using a formal specification language such as statechart, and finally, include a metamodel to allow for scenario modeling.