Surface Anomaly and Intent Forecast System

Surface operations at an airport require careful coordination between pilots and air traffic controllers. Currently, when any deviation from planned routes occurs, the air traffic controller must actively observe and intervene actively to avoid any potential conflicts. The in-time safety assurance goals of NASA’s In-time Aviation Safety Management System (IASMS) necessitate a proactive monitoring and alert tool instead of the reactive alerts provided after an encroachment or incursion occurs. Our innovation offers a robust, extensible surface track monitoring alerting tool that integrates the effective operational configuration, and ATC clearances to identify deviations as soon as they occur. The Surface Anomalies and Intent Forecast (SAIF) tool provides an additional layer of safety assurance over the operations by monitoring surface traffic patterns and ATC-pilot communications to infer deviations by pilots or air traffic controllers. The existing layers of safety provided by ASDE-X and controller interference are reactive in nature and an alert is generated after an incursion or when significant deviation occurs. The proposed SAIF tool leverages the live surface data, ATC-pilot comms, and historic operational patterns to generate a proactive identification of any controller/pilot deviations. Context awareness of SAIF helps alert the controllers to any potential non-compliance immediately after it occurs. We will leverage our expertise in speech-to-text models, real-time data processing, and integrated monitoring to build the Phase I POC for ATL and JFK. The two airports are characterized by complex but varying operational patterns and will be a good proving ground for the technology. The POC will focus on devising a general, extensible, and validated architecture applicable to any airport operations. We will demonstrate and validate the functionality on live data for the two airports by the end of Phase I.