EVOLUTIONARY FRAMEWORK FOR LOAD-AWARE IN-COCKPIT GUIDANCE TOWARDS HIGHLY-AUTOMATED ATC OPERATIONS IN TMAS AND ON THE AIRPORT SURFACE
The aviation sector faces a pivotal challenge: transitioning to higher cockpit automation and enabling Single-Pilot Operations (SiPO) while ensuring safety, efficiency, and manageable workload for pilots and air traffic controllers (ATCOs). This transition demands new avionics, advanced HMIs, AI-based assistants, and redesigned air/ground procedures to safeguard human oversight, particularly during critical phases such as Terminal Manoeuvring Areas (TMAs) and taxiing. EvoFLIGHT responds to this challenge by delivering an evolutionary framework for cockpit automation, human–AI teaming, and integration with ATM systems. EvoFLIGHT aims to develop load-aware, certifiable in-cockpit guidance and assistance that: • maintain or enhance safety in multi-crew and SiPO settings; • reduce cognitive workload and improve response times, even in emergencies; • ensure transparent, explainable automation aligned with EASA’s AI Roadmap; • scale across commuter, business, and mainline aircraft. The project will advance AI-based pilot state monitoring, trajectory prediction, fault-tolerant flight control, and automated take-off/landing to address SiPO risks such as missing cross-checks or incapacitation. Adaptive AI assistants will dynamically allocate ATM-related tasks using physiological and contextual cues. A Flight-Deck ATM Assistant (FDAA) will provide multimodal CPDLC/voice interfaces, tactical trajectory management, “what-if” clearance analysis, and digital support for sustainable taxiing, wake-vortex avoidance, and low-visibility operations. EvoFLIGHT will also extend ATS B2 CPDLC to tactical taxi/departure clearances with push-to-load functionality and automate QNH transmission to eliminate a persistent source of error.
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Imported from OpenAIRE Ukraine | Grant 101290769.
Open sourceOverview
Grant agreement 101290769 | EvoFLIGHT
Open sourceThe aviation sector faces a pivotal challenge: transitioning to higher cockpit automation and enabling Single-Pilot Operations (SiPO) while ensuring safety, efficiency, and manageable workload for pilots and air traffic controllers (ATCOs). This transition demands new avionics, advanced HMIs, AI-based assistants, and redesigned air/ground procedures to safeguard human oversight, particularly during critical phases such as Terminal Manoeuvring Areas (TMAs) and taxiing. EvoFLIGHT responds to this challenge by delivering an evolutionary framework for cockpit automation, human–AI teaming, and integration with ATM systems. EvoFLIGHT aims to develop load-aware, certifiable in-cockpit guidance and assistance that: • maintain or enhance safety in multi-crew and SiPO settings; • reduce cognitive workload and improve response times, even in emergencies; • ensure transparent, explainable automation aligned with EASA’s AI Roadmap; • scale across commuter, business, and mainline aircraft. The project will advance AI-based pilot state monitoring, trajectory prediction, fault-tolerant flight control, and automated take-off/landing to address SiPO risks such as missing cross-checks or incapacitation. Adaptive AI assistants will dynamically allocate ATM-related tasks using physiological and contextual cues. A Flight-Deck ATM Assistant (FDAA) will provide multimodal CPDLC/voice interfaces, tactical trajectory management, “what-if” clearance analysis, and digital support for sustainable taxiing, wake-vortex avoidance, and low-visibility operations. EvoFLIGHT will also extend ATS B2 CPDLC to tactical taxi/departure clearances with push-to-load functionality and automate QNH transmission to eliminate a persistent source of error.