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At least 289 records · Page 16
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NASA NTRS2014

A Novel Application of TMA to Converging Runway Operations in a Simulated NextGen Environment

In 2013, the Airspace Operations Laboratory at NASA Ames Research Center conducted a human-in-the-loop simulation that examined the feasibility of applying a number of Next Generation Air Transportation System (NextGen) solutions to complex arrival operations in and around the New York metroplex. The delivery of arrivals to Newark Liberty International Airport (EWR) was the focus of this simulation, which involved extending the Terminal Sequencing and Spacing (TSS) scheduling capability to precisely schedule arrivals to intersecting runways 22 Left and 11. An important enabler for the concept was the availability of a dependent runway scheduler that was able to coordinate arrival times between aircraft landing on intersecting runways. At the time of the study, there was no functionality within the TSS scheduler to automatically create the dependent runway schedules. Instead, a Traffic Management Coordinator (TMC) manually created a de-conflicted schedule, which allowed for the concept to be tested as well as provided valuable insight into the tool requirements for a dependent runway scheduler. Throughout the course of preparations for the simulation, the individual serving as the TMC developed a number of strategies and procedures for manually adjusting the Scheduled Time of Arrival (STA) of the EWR arrivals in order to ensure that adequate spacing was provided between runway 22L and 11 arrival pairs. This paper describes the strategies and procedures that were developed and details how they were successfully applied during the simulation. Results will also be presented that shed additional light on exactly how the schedules were manipulated and their impact on delivery performance and safety. Ideas for additional TSS enhancements and next steps, based on participant feedback, will also be presented.

Homola, Jeffrey↗
NASA NTRS2014

UAS Integration into the NAS: Phase 1 Human Systems Integration Activities

This presentation addresses the work conducted by the Human Systems Integration (HSI) subproject during Phase 1 of the UAS in the NAS project. This includes information on the HSI technical barriers to UAS integration into the NAS, as well as the HSI objectives that address these barriers. The presentation includes brief discussions of the human-in-the-loop simulations conducted by HSI in Phase 1 and concludes with a look at the activities planned for Phase 2.

human-systems integration↗
NASA NTRS2018

Multi UAS Demo System Example Video

This presentation covers the primary results from a recently completed human-in-the-loop (HITL) simulation conducted as part of the UAS (Unmanned Aircraft Systems) integration into the NAS (National Airspace System) project. The HITL examined the impact of multiple (simultaneous) UAS control while performing a demanding mission task and managing scripted conflicts. The scripted conflicts were designed to trigger the detect-and-avoid (DAA) system. This was the first time the DAA system as designed as part of the UAS-NAS project has been applied to multi-UAS control. The second part of the presentation briefly summarizes the takeaways from two workshops held on human-automation interaction considerations for UAS integration. NASA co-hosted and participated in both workshops.

Rorie, Robert C.↗
NASA NTRS2018

SAM 3.0 Workshop Alliance SV Lab

Nissan is building a Seamless Autonomy Mobility (SAM) which is a cloud-based system of artificial intelligence that manages a fleet of autonomous vehicles. It pairs artificial intelligence with human intelligence in order to create a seamless solution that allows autonomous drive to be viable and marketable. They have created a Mobility Manager function which is a laborer who executed human-in-the-loop functions within SAM system. Nissan invited the HAT Lab to discuss NASA's lessons learned on remote operator support and to participate in a workshop discussion of one of their use cases. This presentation covers some of the HAT Lab's work on a human-autonomy teaming Agent and previous Reduced Crew Operations ground station development.

Human-Autonomy Teaming↗
NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗
NASA NTRS2016

Overview of Sense and Avoid/Separation Assurance Interoperability (SSI)

The UAS in the NAS project is studying the minimum operational performance standards for unmanned aerial systems (UAS's) detect-and-avoid (DAA) system in order to operate in the National Airspace System. The project is executing a series of fast-time simulation, human-in-the-loop experiments, and flight test in support of this effort. Officials of the Swiss Government (Peter Winter, Director of Aeronautical Systems, and Michel Godinat, Head of the Certification Office) are visiting NASA to become more familiar with the advances being made to integrate UAS into civilian airspace. This briefing will review the activities and results over the past 3 years within the Separation AssuranceSense-and-Avoid Interoperability team.

Santiago, Confesor↗
NASA NTRS2016

Simulation and Flight Test Data Collection to Support Phase 1 Detect-and-Avoid MOPS

The UAS Integration into the NAS (UASNAS) project is studying the minimum operational performance standards for unmanned aerial system (UAS) detect-and-avoid (DAA) systems in order to operate in the National Airspace System (NAS). The project is executing a series of fast-time simulation, human-in-the-loop experiments, and flight tests in support of this effort. Armstrong Flight Research Center is hosting a VIP Day in conjunction with the UAS-NAS Project's Flight Test 4 activity, and the Ames Project Engineer (or designate) will be presenting a briefing. This briefing will review the data collection and analysis activities for Flight Test 4 within the Separation assurance-Sense-and-avoid Interoperability (SSI) team.

data analysis↗
NASA NTRS2016

Simulation and Flight Test Data Collection Review for Supporting Phase 1 Detect and Avoid MOPS

RTCA Special Committee 228 is a consortium of government, industry, and academic organizations tasked to develop minimum operational performance standards for UAS detect and avoid systems. The UAS in the NAS (National Airspace System) project is studying the minimum operational performance standards for unmanned aerial systems (UAS's) detect-and-avoid (DAA) system in order to operate in the National Airspace System. Over the past 3 years, the project has executed a series of fast-time simulation, human-in-the-loop experiments, and flight tests in support of this effort. The purpose of this briefing is to summarize the models developed and data collected to overcome UAS integration barriers, so UAS can remain well clear of all traffic.

Santiago, Confesor↗
NASA NTRS2017

Validation of Minimum Display Requirements for a UAS Detect and Avoid System

The full integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS), a prerequisite for enabling a broad range of public and commercial UAS operations, presents several technical challenges to UAS developers, operators and regulators. A primary barrier is the inability for UAS pilots (situated at a ground control station, or GCS) to comply with Title 14 Code of Federal Regulations sections 91.111 and 91.113, which require pilots to “see and avoid” other aircraft in order to maintain well clear. UAS pilots’ removal from the flight deck of the aircraft necessitates the development of a UAS-specific system for detecting nearby traffic and displaying traffic information to the pilot to support their ability to maintain an objectively defined “DAA well clear” threshold from other aircraft. This new UAS-specific function of remaining DAA well clear is called traffic avoidance. The resulting Detect and Avoid (DAA) system, however, will be subject to a collection of requirements that manufacturers will be obligated to meet in order to certify their equipment. RTCA Special Committee 228 (SC-228), a consortium of representatives from government, industry and academia, is responsible for developing and documenting the Minimum Operational Performance Standards (MOPS) for UAS DAA systems. The present study is the final in a series of human-in-the-loop (HITL) experiments designed to explore and test the various display and alerting requirements being incorporated into the DAA MOPS. Whereas the prior DAA HITLs examined a wide variety of DAA display features and concepts, the current experiment aims to validate the latest minimum display requirements for Phase 1 of the DAA MOPS. Rather than test different display concepts, this study tests two configurations of a MOPS-compatible DAA display: a version that is integrated into the primary navigation and control display of the GCS and a version that is physically separated from the primary display. This manipulation tests the draft minimum requirement that allows the DAA traffic display to be a separate, or standalone, configuration. This type of configuration is a more achievable near-term technology solution since it does not stipulate additional certification or integration requirements on UAS manufacturers. However, a standalone display configuration has the potential to result in pilot performance issues resulting from the cognitive costs of switching between the primary DAA display and the primary navigation and control display. This configuration is also particularly susceptible to errors if the displays are in different orientations (e.g., north-up versus track-up). Both the integrated and standalone display configurations were presented to 16 active UAS pilots in a medium-fidelity simulation, which included confederate air traffic controllers and pseudo pilots operating simulated manned traffic. Pilots were tasked with navigating two different mission routes while maintaining DAA well clear with scripted conflicts. Pilot response times (i.e., measured response) and ability to remain DAA well clear are reported. Primary results indicate that both display configurations resulted in favorable response times and well clear rates. While there were clear trends of pilots objectively performing better in the integrated display condition, with several measured response metrics reaching statistical significance, the differences between the two displays were typically moderate. While the primary variable of DAA display location did not have an especially large impact on pilot performance on its own, when examined alongside the type of DAA threat the pilot was facing (a caution-level versus a warning-level alert), the response time benefits associated with the integrated display were amplified. The implications of these American Institute of Aeronautics and Astronautics 2 results on the Phase 1 DAA MOPS and the connection of this data to previous studies is also discussed.

detect and avoid↗
NASA NTRS2015

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.

Lee, Hanbong↗
NASA NTRS2015

Enabling Performance-Based Navigation Arrivals: Development and Simulation Testing of the Terminal Sequencing and Spacing System

NASA has developed an advanced arrival management capability for terminal controllers, known as Terminal Sequencing and Spacing (TSS). TSS increases use of performance-based navigation (PBN) arrival procedures during periods of high traffic demand. It extends two Federal Aviation Administration's operational systems with terminal metering and controller spacing tools. Sixteen high-fidelity human-in-the-loop simulations, involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of- concept design to fully functional prototype. These simulations modeled arrival procedures at several U.S. airports, incorporated a broad range of traffic demand profiles and wind conditions, and used controllers with extensive operational experience. Two fundamental metrics are evaluated for these simulations: PBN Success Rate and Inter-Arrival Spacing Error. The PBN Success Rate shows a definitive trend when TSS is used. It increases from 42 percent for today's operations to 68 percent for terminal metering only and 92 percent for terminal metering with controller-managed spacing tools. Meanwhile, the Inter-Arrival Spacing Error improves 25 to 35 percent when TSS is used compared to not used. The TSS technology was transferred to the FAA and, and it is targeted for deployment to several busy airports in the U.S. starting in 2018.

terminal metering↗
NASA NTRS2019

A Detect and Avoid System in the Context of Multiple-Unmanned Aircraft Systems Operations

NASA's Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgements about a potential threat of a loss of well clear with another aircraft. NASA's Phase 1 research supported the development of a Detect and Avoid (DAA) system that supports the ground-based pilot's ability to detect potential traffic conflicts and determine a resolution maneuver, but existing display/alerting requirements did not account for multiple UAS control (1:N). Demands for increased scalability of UAS in the NAS operations are expected to create a need for simultaneous control of UAs, and thus, a new DAA HMI design will likely be necessary. Previous research, however, has found performance degradations as the number of vehicles under operator control has increased. The purpose of the current human-in-the-loop (HITL) simulation was to examine the viability of 1:N operations with the Phase 1 DAA alerting and guidance. Sixteen UAS pilots flew three scenarios with varying number of UAs under their control (1:1, 1:3, 1:5). In addition to their supervisory and sensor mission responsibilities, pilots were to utilize the DAA system to remain DAA well clear (DWC) during scripted conflicts of mixed severity. Measured response times, separation performance, mission task data, and subjective feedback were collected to assess how the multi-UAS control configuration impacted pilots' ability to maintain DAA well clear and perform the mission tasks. Overall, the DAA system proved surprisingly adaptive to multi-UAS control for preventing losses of DAA well clear (LoDWC). The findings suggest that, while multi-UAS operators are able to maintain safe separation (DWC) from other traffic, their ability to efficiently perform missions drastically decreases with their number of controlled vehicles. Pilot feedback indicated that, for this context, the use of automation support tools for completing and managing mission tasks would be appropriate and desired, especially for ensuring efficient use of assets. Finally, human-machine interface (HMI) design considerations for multi-UAS operations are discussed.

Monk, Kevin J.↗
NASA NTRS2015

Spot and Departure Runway Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for Dallas-Ft. Worth International Airport. The presentation also discusses the latest status of NASA's current surface research through a collaboration with an airline partner, where a tool is developed for airline ramp operators to assist departure pushback operations.

Departure Schedule↗
NASA NTRS2015

Human Factors Assessment of Disturbances to Scheduled Performance-Based Navigation Arrival Operations

The introduction of Performance-Based Navigation (PBN) specifications to air traffic management has resulted in many benefits during nominal operations, including shorter flight paths, reduced fuel costs, and improved terminal area arrival rates. However, these benefits become less noticeable during off-nominal operations where aircraft are routinely interrupted from staying on PBN procedures due to disturbances such as missed approaches. This human-in-the-loop (HITL) study used multiple types of disturbance events to perturb the arrival schedule. Perturbed schedules were managed with different types of schedule adjustments, including a condition with no adjustments. The study collected data on a host of dependent variables, including human factors measures on controller workload and system performance measures such as schedule nonconformance (nc). Initial analyses showed strong correlations between aggregated controller workload and aggregated nc, as well as benefits of both automatic and manual schedule adjustments for increasing system performance, such as reduced PBN procedure interruptions. The goal of this paper is to further test these initial findings. The results indicated that an increase in schedule nonconformance correlated with an increase in controller workload at specific time intervals, and automated schedule adjustments consistently reduced controller workload associated with nonconformance.

Human factors↗
NASA NTRS2015

Terminal Sequencing and Spacing (TSS)

The Federal Aviation Administration's (FAA) Next Generation Air Transportation System (or NextGen) is being designed to support the predicted increases in traffic volume and to increase the capacity, efficiency and safety of the National Airspace System (NAS). The Federal Aviation Administration (FAA) identifies Performance-Based Navigation (PBN) as a key enabling capability of NextGen and is actively publishing PBN procedures at major airports throughout the United States. Standard Terminal Arrival Routes (STARs), procedures, and approaches are designed to facilitate fuel-efficient continuous descent operations. However, their use is limited during periods of high traffic demand due to the complexity of merging multiple streams of aircraft to the same airport. As a result, most arrivals in the Terminal Radar Approach Control (TRACON) area continue to be controlled using radar vectoring and step-down descents, resulting in high workload for controllers and diverting aircraft from efficient PBN trajectories. To address this issue, NASA developed the Terminal Sequencing and Spacing (TSS) system, an advanced arrival management technology that combines time-based scheduling and controller-based precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals on Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. The two main components of TSS are: 1) a scheduler that de-conflicts merging arrivals in the terminal area by computing appropriate arrival times to the runway threshold and upstream terminal merge points, and 2) a set of Controller-Managed Spacing (CMS) decision support tools to efficiently assist schedule conformance. Sixteen high-fidelity human-in-the-loop simulations involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of-concept design to a fully functional prototype. Results indicate high controller use and acceptability of the CMS tools as well as improved PBN route conformance (Figure 2). The TSS technology was transferred to the FAA in 2014, and it is targeted for deployment to several busy airports in the U.S. starting in 2018. Potential enhancements to TSS using DataComm will also be presented.

controller-managed spacing↗
NASA NTRS2014

Controller Inhibition of Automated Conflict Resolutions in a Maximum NextGen Condition

With the continued projection of increases in air traffic density, operations in the National Airspace System are expected to exceed human capabilities in the near future. In order to address the bottleneck of human workload capacity, highly automated safety-critical systems are under development to support air traffic controllers. A human-in-the-loop experiment examined controllers transition through four NextGen automation stages: Current-Day, Minimum, Moderate, and Maximum. Maximum NextGen simulated a fully automated environment where the automation was responsible for detecting and resolving conflicts within simulation parameters in high-density airspace. By allocating these tasks to the automation, the controllers task changed. The human moved to primarily a supervisory position- typically only regaining control over separation assurance tasks during conflict situations deferred by the automation. While tasks were allocated a-priori between the controller and automated agent, controllers maintained authority to inhibit the automation from interacting with particular aircraft. Preliminary work is complete, where significant differences were found in inhibition frequency between simulation participants. However, the contexts in which the controllers inhibited the automation, and their reasons for doing so, remain unclear. This analysis attempts to identity factors contributing to human controllers inhibition of the automation in the Maximum NextGen condition.

adaptable automation↗
NASA NTRS2015

Tool-Enabled Changes in Terminal Air Traffic Controller Task and Workload Distribution

A human-in-the-loop simulation was conducted that examined an approach to adjusting airport arrivals in such a way as to enable higher departure throughput while maintaining arrival throughput. This approach, referred to as Departure-Sensitive Arrival Spacing (DSAS), leverages the capabilities of the Terminal Sequencing and Spacing (TSS) system with an additional Decision Support Tool (DST) to assign more precise arrival spacing interval. This presentation will focus on the changes observed in the task distribution and control strategies among three Terminal Radar Approach Control (TRACON) controller participants and the workload associated with those changes across three conditions: Baseline, TSS, and DSAS. Results showed that the application of the DSTs in the TSS condition and those in the DSAS condition enabled a progressive reduction in the number of clearances issued and for the clearances to be issued earlier, further away from the airport. In doing so, there was also a greater use of speed control versus vectoring and altitude for schedule conformance in the TSS and DSAS conditions respectively and relative to Baseline. Workload also shifted in conjunction with the clearance distribution with an overall reduction across conditions. The changes in task and workload distribution enabled a shift from tactical to strategic control, which allowed for a more predictable and efficient delivery of arrivals.

task↗
NASA NTRS2015

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors: An Interrupted Time-Series Analysis of Controller Behavior

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASAs Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlantas Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the studys trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 20-knot over-predictions and 20-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

human-automation interaction↗