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Ontological Decision-Making Support for Air Traffic Management

IEEE (SMIEEE), FHEA (Fellow of the Higher Education Academy, UK)

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Air Traffic Management (ATM) is a vital service in air transportation provided by Air Traffic Controllers (ATCs). ATCs and aircraft pilots are required to make increasingly difficult airspace decisions based on an ever-increasing number of information inputs such as weather, flights, airspace, and reports. Additionally, the proliferation of Unmanned Aerial Systems (UASs) raises concerns about safety and security (including cybersecurity) that challenge future airspace control. Additionally, ATM decision making requires dynamic situational awareness provided by mental cognitive faculties of airspace personnel. This lecture presents a Situation Awareness (SAW) Decision support system ATM Reasoner (SAWDAR) approach to support decisional processes in ATM. The SAWDAR approach is based on artificial cognition implemented by means of knowledge representation (ontologies) and semantic reasoning. The ontological SAWDAR approach incorporates uncertainty considerations by including metrics for the veracity of surveillance systems such as radars and automatic dependent surveillance – broadcast (ADS-B). By using the ontological SAW, experimental results demonstrate enhanced decision performance on airspace situations in a scenario where an airplane is about to take-off in the presence of potential hazardous drones flying around. Thus, the ontological SAWDAR approach can autonomously and dynamically support stakeholders (ATCs and pilots) to decide whether to allow aircraft take off. The approach gives place to an Avionics Analytics Ontology (AAO) towards NextGen flight management.