NASA (https://www.nasa.gov/aeroresearch/programs/aosp/atm-x/atm-x-project-description) has launched its Air Traffic Management – eXploration (ATM-X) project which “will transform the air traffic management system to safely accommodate the growing demand of new air vehicles to enter the airspace to perform a variety of missions,” said the agency.
“ATM-X also works on technologies to allow the traditional large commercial aircraft to fly more user-preferred routes with improved predictability, resulting in fuel and time savings. ATM-X is currently in Phase 1 of its research objectives – exploring challenging use cases (e.g., high-density – crowded – vertiport operations for urban air mobility) to identify parameters for best performance and to prioritize the key technical challenges that must be solved in order to achieve those parameters.”
“In Phase 2, ATM-X will explore solutions to those technical challenges by developing, testing and then transferring key concepts and technologies to stakeholders in the aviation community. Phase 2 will also see demonstrations of:
- An open architecture approach
- Integration of air traffic technologies
- System-wide data use
- Advances in human-machine teaming
- Increasingly autonomous decision-making
“The goal is to show that these technologies, together, provide comprehensive situational awareness in a more crowded airspace, and improve coordinated decision-making and disruption management through the use of advisories. ATM-X partners with the FAA to leverage new technologies into their infrastructure modernization investments, and leverages knowledge gained from NASA’s Airspace Technology Demonstrations and System-Wide Safety projects. ATM-X will also develop and demonstrate the fourth and final technical capability level (TCL4) for Unmanned Aircraft Systems Traffic Management (UTM). This final UTM demonstration in 2019 will involve higher-density urban areas for autonomous vehicles used for newsgathering and package delivery, and also will test technologies that could be used to manage large-scale contingencies.”
