The Future of Air Traffic Management
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An Implementation Analysis of Communications, Navigation, and Surveillance (CNS) Technologies for Unmanned Air Systems (UAS)
The aviation industry and government agencies face a rapidly-emerging need for integrating large-scale populations of Unmanned Air Systems (UAS) into the worldwide controlled and uncontrolled airspace. Critical components for integration include the Communications, Navigation, and Surveillance (CNS) technologies necessary for ensuring safe UAS operations. Under NASA program NNA16BD84C, our work on CNS architectural concepts for the safe operation of UAS in controlled and uncontrolled airspace has introduced CNS architectures which must be analyzed in terms of implementation readiness.Controlled airspace operations for UAS are consistent with the needs for manned aviation in the worldwide Air Traffic Management (ATM) service. Uncontrolled airspace operations are consistent with the NASA Unmanned (air) Traffic Management (UTM) concept of operations. Implementation readiness is based on the NASA concept of Technology Readiness Levels (TRLs) ranging from TRL1 (basic principles observed and reported) to TRL9 (actual system flight proven through successful mission operations). In the architecture concepts, we have introduced a number of new CNS architectural elements which need to be correlated with TRL levels.In this paper, we present our implementation analysis for communications networks, communications data links, navigation, and surveillance. Each area has been under active research and development during the course of the current NASA program which has produced studies on UAS CNS Requirements, UAS CNS Architecture for Controlled Airspace and UAS CNS Architecture for Uncontrolled Airspace. We have published our architecture concepts in major UAS-related conferences (including iCNS2017, IEEE Aerospace 2018, and iCNS2018) and will continue to seek additional publication opportunities. We look forward to continuing our work to realize a full integration testing scenario for both controlled and uncontrolled airspace operation.
Ponchak, Denise↗
ATM-X: Air Traffic Management – eXploration: Urban Air Mobility Overview
The UAM market is expected to be big in the billions of dollars. And, of course, UAM will need to be able to readily access and operate in the airspace for this to happen. In the ATM-X UAM sub-project, we are focused on NASA’s top-level goal of enabling UAM Maturity Level-4 (UML-4) operational density and complexity, with hundreds of simultaneous operations in a metropolitan area managed through a UTM-like architecture with third-party provided traffic management services. We are doing this by developing a concept of operations with the FAA and the UAM community that serves as a framework and guide for community research and development. We are also doing this by conducting research to define system requirements based on the concept and developing the UAM airspace management system for the AAM National Campaign.
urban air mobility↗
The Path to NGATS
NGATS operational Improvements and benefits include: 1) Broad area and precision navigation to access and capacity; 2) Airspace access and management to capacity; 3) 4D trajectory based ATM to capacity and efficiency; 4) Reduced separation between aircraft to capacity; 5) Flight deck situational awareness and delegation to capacity and safety; 6) ATM decision support to capacity; 7) Improved weather data and dissemination to capacity and safety; 8) Reduced cost to deliver ATM services to cost; 9) Greatly expanded airport network and improved terminals to capacity.
Scardina, John↗
Spectrum Research for Aviation Final Report
Task TO-G-026 was established to identify possible spectrum allocations to support the Scalable Traffic Management for Emergency Response Operations (STEReO) Project’s approach of applying Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concepts to the management of disaster response. Both large and small unmanned aircraft have been found to provide significant benefits in disaster response. However, application of unmanned aircraft is limited by the difficulty in managing disaster area airspace. UTM concepts can greatly increase the ability to safely apply UAS to many disaster response requirements. The use of UAS requires communication links to provide command and control, telemetry, delivery of mission payload data, and situational awareness/navigation functionality to enable safe operations. The outcome of Task TO-G-026 research provides guidance in identifying appropriate potential spectrum bands required to support STEReO disaster response operations. This report presents these research results.
Robert J. Kerczewski↗
Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility
The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.
Edge computing↗
Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility
The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.
Edge computing↗
Safety Related Considerations in Autonomy
In this talk I will describe NASA strategy and research efforts to provide safety assurance for increasingly autonomous systems used in aviation. In the near future, autonomy will play an important role in civil aviation, and its applications will range from vehicles and platforms (UAVs, transport-class, including supersonic to hypersonic, aircraft) to airspace operations, or health management systems. This infusion of autonomy is driven by a need for optimizing airspace operations to accommodate increasing traffic density (e.g., adaptive trajectory-based operations, autonomous tugs, close parallel runways, and dynamic separation assurance), reducing operation costs to ensure that US operators can compete with emergent countries, and enabling new business models (e.g., fire fighting, UAS-based package delivery and precise aerial photography). In essence virtually every component of the National Airspace System will become increasingly autonomous. Yet we need to do so in a safe manner and have techniques and processes in place to ensure the safety of the public. This talk describes NASA plans to address this problem
Air Traffic Management↗
Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace
Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.
Upper Class E Traffic Management↗
Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace
Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.
Upper Class E Traffic Management, ETM, Cooperative↗
Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control
Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.
Regina Ayoubi↗
ETM: Upper Class E Traffic Management
This is a slide set as part of a meeting series with members of a working group aimed at the development of a concept that addresses needs and gaps in the management of high-altitude airspace operations. This concept leverages elements developed through the UAS Traffic Management project with respect to a cooperative, service-based approach that provides services and capabilities in areas (e.g., Upper Class E airspace) that currently receive no or limited service from Air Traffic Control. This concept is meant to provide a safe, fair, and scalable approach to management of Upper Class E operations that reduces the burden on ATC while providing the flexibility and access desired by current and new users of the airspace. This set of slides includes an overview of discussions and industry news covering the time since the previous group meeting, an overview of relevant activities by NASA's Airborne Science Program, a review of the 2023 ETM Workshop, a placeholder for a presentation on an approach to an International Framework for operations, and an update on NASA's preparations for upcoming simulations.
Upper E↗
Identifying Common Coordination Procedures across Extensible Traffic Management (xTM) to Integrate xTM Operations into the National Airspace System
New categories of missions and vehicle types, such as drone delivery services, on-demand air taxi, and high-altitude long-endurance (HALE) vehicles are being proposed to operate using a novel, highly automated information exchange infrastructure and a community-based, cooperative traffic management concept. Collectively, these new operations are called Extensible Traffic Management (xTM). As these xTM vehicles become more prevalent, their operations will increasingly overlap with existing conventional aircraft and with each other. In order to seamlessly co-exist with current conventional aircraft operations, new coordination procedures, tools and services will be needed to integrate xTM into the future National Airspace System (NAS). In our prior work, we have identified a set of use cases for xTM interactions with air traffic control (ATC), categorized across different xTM operations based on trigger events. Events consisted of ones such as nominal xTM vehicle transition into the ATC environment or an off-nominal emergency landing situation. In this paper, we have extended the prior work to identify commonalities in the coordination procedures across xTM, as well as differences that are specific to the individual xTM operations. The overall results showed that two types of xTM-ATC interactions were prevalent: 1) xTM vehicles transitioning between xTM and ATC operational environments; 2) xTM vehicles being allowed to continue xTM operations in areas that are normally controlled by ATC. The results also suggested that emergency and rare off-nominal events may need specialized procedures for each vehicle type. The overall results suggest that there is a pathway to define a common method of handling and integrating diverse xTM operations in the future NAS, but there need to be procedures for individualized handling of xTM vehicles in infrequent, safety-critical events.
Extensible Traffic Management (xTM)↗
Simulations of Continuous Descent Operations with Arrival-management Automation and Mixed Flight-deck Interval Management Equipage
Air traffic management simulations conducted in the Airspace Operations Laboratory at NASA Ames Research Center have addressed the integration of trajectory-based arrival-management automation, controller tools, and Flight-Deck Interval Management avionics to enable Continuous Descent Operations (CDOs) during periods of sustained high traffic demand. The simulations are devoted to maturing the integrated system for field demonstration, and refining the controller tools, clearance phraseology, and procedures specified in the associated concept of operations. The results indicate a variety of factors impact the concept's safety and viability from a controller's perspective, including en-route preconditioning of arrival flows, useable clearance phraseology, and the characteristics of airspace, routes, and traffic-management methods in use at a particular site. Clear understanding of automation behavior and required shifts in roles and responsibilities is important for controller acceptance and realizing potential benefits. This paper discusses the simulations, drawing parallels with results from related European efforts. The most recent study found en-route controllers can effectively precondition arrival flows, which significantly improved route conformance during CDOs. Controllers found the tools acceptable, in line with previous studies.
arrival management↗
Human Interfaces and Management of Information (HIMI) Challenges for “In-time” Aviation Safety Management Systems (IASMS)
The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.
Lawrence J Prinzel↗
UAS Contingency Management: The Effect of Different Procedures on ATC in Civil Airspace Operations
Current study examined the impact of four, currently-employed, unmanned aerial system (UAS) contingency procedures on air traffic controller (ATC) workload and performance. Controllers managed a Terminal Radar Approach Control (TRACON) sector while a single UAS passed through their sector. In four of the five scenarios, the UAS was scripted to experience an emergency (e.g., loss of command and control link, sudden drop in oil pressure) and then execute one of four possible contingency maneuvers. The fifth scenario was a baseline and contained no emergency. Results showed no significant differences in performance or workload among the four the study's design discussed.
Contingency Management↗
En route Spacing Tool: Efficient Conflict-free Spacing to Flow-Restricted Airspace
This paper describes the Air Traffic Management (ATM) problem within the U.S. of flow-restricted en route airspace, an assessment of its impact on airspace users, and a set of near-term tools and procedures to resolve the problem. The FAA is committed, over the next few years, to deploy the first generation of modem ATM decision support tool (DST) technology under the Free-Flight Phase-1 (FFp1) program. The associated en route tools include the User Request Evaluation Tool (URET) and the Traffic Management Advisor (TMA). URET is an initial conflict probe (ICP) capability that assists controllers with the detection and resolution of conflicts in en route airspace. TMA orchestrates arrivals transitioning into high-density terminal airspace by providing controllers with scheduled times of arrival (STA) and delay feedback advisories to assist with STA conformance. However, these FFPl capabilities do not mitigate the en route Miles-In-Trail (MIT) restrictions that are dynamically applied to mitigate airspace congestion. National statistics indicate that en route facilities (Centers) apply Miles-In-Trail (MIT) restrictions for approximately 5000 hours per month. Based on results from this study, an estimated 45,000 flights are impacted by these restrictions each month. Current-day practices for implementing these restrictions result in additional controller workload and an economic impact of which the fuel penalty alone may approach several hundred dollars per flight. To mitigate much of the impact of these restrictions on users and controller workload, a DST and procedures are presented. The DST is based on a simple derivative of FFP1 technology that is designed to introduce a set of simple tools for flow-rate (spacing) conformance and integrate them with conflict-probe capabilities. The tool and associated algorithms are described based on a concept prototype implemented within the CTAS baseline in 1995. A traffic scenario is used to illustrate the controller's use of the tool, and potential display options are presented for future controller evaluation.
Green, S.↗