Advanced Air Mobility (AAM) research requires careful attention and investigation to enable future autonomous air traffic. This is in line with the Federal Aviation Administration's (FAA's) vision to allow both manned and autonomous operations in a mixed-use airspace with careful separation of air corridors and negotiated rules for conflict resolution (e.g. in the terminal airspace above an airport). The FAA plans to open up the skies with an InfoCentric National Airspace System (NAS), but the air users have to make best use of this information and play by the rules to continue the strong safety record that we currently enjoy.
NASA's vision for AAM is to help emerging aviation markets to safely develop an air transportation system that moves people, packages, and cargo between places previously not served or underserved by aviation – local, regional, intraregional, urban – using revolutionary new aircraft, hybrid propulsion and communication/self-organization methods that are only just now becoming possible.
This Research Topic is formulated to address some of the fundamental barriers that need to be solved to enable AAM and is intended to highlight key research advances in the areas of hybrid propulsion, energy management, aeroacoustics, communication, and conflict management.
The focus of research submitted to this Research Topic will be on developing the required scalable AI and sensible ML technologies/methods (e.g. to determine the optimal mode switching of hybrid propulsion vehicles, when to operate in battery mode for noise abatement vs when to use gas mode to charge the battery).
Research included can also look at self-scheduling methods (e.g. conflict detection, resolution, and collision avoidance methodologies) that will enable a large number of autonomous vehicles to operate safely and efficiently in a common airspace without relying on a human arbiter (ATC). In this regard, the focus will be on developing inter vehicle negotiation and conflict resolution rules and evasive maneuvers that both parties agree upon, coordinate and implement independently.
Lastly, research on AAM-ATC interactions which will look at communication methods between autonomous aircraft and ATC is welcome. Since current day ATC/pilot communications are primarily voice based, unmanned aircraft must be able to understand voice-based commands and relay back information that is interpretable by the human operators in the system. This requires novel Automatic Speech Recognition (ASR) methods trained on Aviation data and digitization of legacy voice and text-based information that can be directly ingested into automated systems (e.g., route planners) onboard the aircraft. To this end, this Research Topic will highlight the development and maturation of automation (AI/ML/NLP) technologies and architectures that support AAM operations.
Research submitted to this collection should specifically focus on how the automation can increase safety, efficiency, and scalability of flight operations in future mixed-use high density air traffic environment.
Advanced Air Mobility (AAM) research requires careful attention and investigation to enable future autonomous air traffic. This is in line with the Federal Aviation Administration's (FAA's) vision to allow both manned and autonomous operations in a mixed-use airspace with careful separation of air corridors and negotiated rules for conflict resolution (e.g. in the terminal airspace above an airport). The FAA plans to open up the skies with an InfoCentric National Airspace System (NAS), but the air users have to make best use of this information and play by the rules to continue the strong safety record that we currently enjoy.
NASA's vision for AAM is to help emerging aviation markets to safely develop an air transportation system that moves people, packages, and cargo between places previously not served or underserved by aviation – local, regional, intraregional, urban – using revolutionary new aircraft, hybrid propulsion and communication/self-organization methods that are only just now becoming possible.
This Research Topic is formulated to address some of the fundamental barriers that need to be solved to enable AAM and is intended to highlight key research advances in the areas of hybrid propulsion, energy management, aeroacoustics, communication, and conflict management.
The focus of research submitted to this Research Topic will be on developing the required scalable AI and sensible ML technologies/methods (e.g. to determine the optimal mode switching of hybrid propulsion vehicles, when to operate in battery mode for noise abatement vs when to use gas mode to charge the battery).
Research included can also look at self-scheduling methods (e.g. conflict detection, resolution, and collision avoidance methodologies) that will enable a large number of autonomous vehicles to operate safely and efficiently in a common airspace without relying on a human arbiter (ATC). In this regard, the focus will be on developing inter vehicle negotiation and conflict resolution rules and evasive maneuvers that both parties agree upon, coordinate and implement independently.
Lastly, research on AAM-ATC interactions which will look at communication methods between autonomous aircraft and ATC is welcome. Since current day ATC/pilot communications are primarily voice based, unmanned aircraft must be able to understand voice-based commands and relay back information that is interpretable by the human operators in the system. This requires novel Automatic Speech Recognition (ASR) methods trained on Aviation data and digitization of legacy voice and text-based information that can be directly ingested into automated systems (e.g., route planners) onboard the aircraft. To this end, this Research Topic will highlight the development and maturation of automation (AI/ML/NLP) technologies and architectures that support AAM operations.
Research submitted to this collection should specifically focus on how the automation can increase safety, efficiency, and scalability of flight operations in future mixed-use high density air traffic environment.