The High Density Vertiplex Advanced Onboard Automation Overview
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Urban Air Mobility↗
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Visual and Inertial Datasets for an eVTOL Aircraft Approach and Landing Scenario
A National Aeronautics and Space Administration (NASA) project developing computer vision algorithms for autonomous flight is producing real-world datasets with cameras mounted on aircraft. In related domains, such as autonomous driving, open datasets are key to innovation and advancement in computer vision and autonomous perception for future Advanced Air Mobility (AAM) operations. Few vision datasets, however, are publicly available in the aviation context. This paper introduces preliminary datasets containing several examples of approach and landing scenarios. The platform aircraft include a multirotor small unmanned aerial system (sUAS) and a crewed helicopter as surrogates for future electric vertical take-off and landing (eVTOL) aircraft. The dataset provides video imagery with associated inertial navigation system-global positioning system (INS-GPS) position and attitude estimates and other sensors. Surveyed locations of the visual features of the landing area are included. This dataset is the first to be released in an ongoing effort to collect and share large, diverse datasets relevant to autonomous aviation; community critique that can inform and improve future flight campaigns is welcome.
Nelson Brown↗
An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm
Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.
Chester Dolph↗
Classifying Aircraft using Velocity Data with Support Vector Machines and Likelihood Ratio Tests
Timely classification of aircraft is important for small unmanned aerial system (sUAS) technologies, such as onboard collision avoidance systems, and aerial perimeter security for prisons and sports venues. This work uses velocity-based metrics to classify multi-rotor sUAS, fixed wings UAS, and general aviation planes using two classification methods: Support Vector Machines (SVM), and Likelihood Ratio (LR) tests. We found that a 96% classification accuracy is achieved when either classifier is trained using average speed derived from flight controller data or radar data and tested with one second of radar data. Further, we show that LR tests perform similarly to SVM for single metric classification. In addition, we present two novel metrics for classifying aircraft: log variance of absolute change in speed, and log variance of relative change in speed. Finally, we discuss challenges associated with training classifiers with flight controller data but testing on radar data.
Logan T Dihel↗
Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations
This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.
Chester V. Dolph↗
Making the Case for New Research to Support the Integration of Small Unmanned Aircraft Systems into the National Airspace System
This paper describes the current state of sUAS regulation, their technical capabilities and the latest technologies that will allow for sUAS NAS integration. The research that is needed to demonstrate sUAS NAS integration capability is identified, and recommendations for conducting this necessary research are suggested.
McAdaragh, Raymon M.↗
Unmanned Aircraft Systems (UAS) Traffic Management Project (UTM) Small UAS Communications and Navigation
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sUAS↗
UTM and D-NET: NASA and JAXA's Collaborative Research on Integrating Small UAS with Disaster Response Efforts
Natural disasters, such as flooding, wildfire, hurricane, tornadoes, earthquakes and tsunamis, pose challenges in preserving human life and minimizing the damages to a region. During catastrophic events, timely response of disaster relief personnel, an efficient deployment of resources in the recovery effort, and coordinated information sharing amongst different relief agencies can make a substantial difference in responding to those impacted by the disaster. Many relief activities currently utilize both ground personnel and manned airborne assets during different phases of the disaster response. Typically, multiple organizations support relief activities and this often creates logistics coordination challenges between agencies which can result in wasted time or resources. The Japan Aerospace Exploration Agency (JAXA) has been developing an "Integrated aircraft operation system for disaster relief (D-NET)", which assists collection and sharing of disaster information through the integrated operation of aircraft such as helicopters, aircraft, and satellites, for efficient and safe rescue operations by disaster relief aircraft. Due to the advancement in unmanned aircraft systems (UAS) technologies, public safety organizations have started incorporating small UAS (sUAS) as an asset in their disasters response activities. To address the airspace integration challenges of the influx of sUAS in the United States the National Aeronautics and Space Administration (NASA), under the UAS Traffic Management (UTM) project, has been engaged in research to enable large-scale commercial applications of sUAS operating in low altitude airspace. This paper presents the integration of D-NET, which incorporate sUAS in the planning, information sharing, and operation support of disasters response activities, and UTM, which provides airspace management to enable large scale high density operations. The integration of the DNET and UTM systems enables coordination, data sharing, and airspace management to improve the timeliness of the disaster response, enable relief organization to reduce cost and overhead by using UAS assets and still maintain airspace safety during the relief activities.
UTM↗
Obtaining Public Opinion about sUAS Activity in an Urban Environment
The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, a series of flight test demonstrations were organized over five years at seven test sites. Technology Capability Level-4 (TCL4), the most complex flight tests, were conducted in Texas, USA, during August 2019. This testing resulted in over 400 data collection flights using eight live rotorcraft, with nine flight crews flying pre-planned scenarios in the urban downtown and waterfront areas of Corpus Christi, Texas. Test scenarios were designed to include a variety of elements, including live and simulated vehicles, and personnel in many different roles, including flight crews and mission personnel. One group of people who did not have a role in the flight tests but will be affected by UAS operations as they become more ubiquitous, are the general public. What do the public think about sUAS operations in urban areas? In order to obtain a reference point that noted the public's view of the operations in the TCL4 demonstration, a short survey was developed and offered to members of the public who wished to comment on the sUAS activities they saw in their city during two weeks in August, 2019.Forty five people completed the online public opinion survey. Participants volunteered to take it, creating a self-selected sample. The survey included eleven questions that asked participants about their level of comfort and concerns with UAS activity; their knowledge of UAS operations; and, whether a traffic management system like UTM would increase their confidence in urban UAS activity. The general public in Corpus Christi showed a good level of knowledge of sUAS regulations, with almost half of their responses to the knowledge portion of the survey being correct. They expressed a moderate level of concern (x = 4.7 out of 7) at urban sUAS activity and, of those who cited a concern, the majority reported this was about privacy (54%), which are all responses in line with those reported in earlier research on public opinion. Views on UTM were mixed, with respondents indicating they thought the introduction of UTM will improve safety somewhat (5.2 out of 7) but it will also increase their concern a little (4.6 out of 7). This would indicate there needs to be more information and educational material about the UTM concept made available to the public with the aim to reduce public concerns about the system.
Caterina Grossi↗
Obtaining Public Opinion About sUAS Activity in an Urban Environment
The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, a series of flight test demonstrations were organized over five years at seven test sites. Technology Capability Level-4 (TCL4), the most complex flight tests, were conducted in Texas, USA, during August 2019. This testing resulted in over 400 data collection flights using eight live rotorcraft, with nine flight crews flying pre-planned scenarios in the urban downtown and waterfront areas of Corpus Christi, Texas. Test scenarios were designed to include a variety of elements, including live and simulated vehicles, and personnel in many different roles, including flight crews and mission personnel. One group of people who did not have a role in the flight tests but will be affected by UAS operations as they become more ubiquitous, are the general public. What do the public think about sUAS operations in urban areas? In order to obtain a reference point that noted the public’s view of the operations in the TCL4 demonstration, a short survey was developed and offered to members of the public who wished to comment on the sUAS activities they saw in their city during two weeks in August, 2019. Forty five people completed the online public opinion survey. Participants volunteered to take it, creating a self-selected sample. The survey included eleven questions that asked participants about their level of comfort and concerns with UAS activity; their knowledge of UAS operations; and, whether a traffic management system like UTM would increase their confidence in urban UAS activity. The general public in Corpus Christi showed a good level of knowledge of sUAS regulations, with almost half of their responses to the knowledge portion of the survey being correct. They expressed a moderate level of concern (x = 4.7 out of 7) at urban sUAS activity and, of those who cited a concern, the majority reported this was about privacy (54%), which are all responses in line with those reported in earlier research on public opinion. Views on UTM were mixed, with respondents indicating they thought the introduction of UTM will improve safety somewhat (5.2 out of 7) but it will also increase their concern a little (4.6 out of 7). This would indicate there needs to be more information and educational material about the UTM concept made available to the public with the aim to reduce public concerns about the system.
UTM↗
Detection and Tracking of Aircraft in the Far-Field from Small Unmanned Aerial Systems
Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small Unmanned Aerial System (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with FAA well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of Linear Kalman-filtering and Extended Kalman-filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS vs. fixed-wing sUAS and multirotor sUAS vs. general aviation(GA)plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.
Unmanned Aerial System↗
Initial Investigation into the Psychoacoustic Properties of Small Unmanned Aerial System Noise
For the past several years, researchers at NASA Langley have been engaged in a series of projects to study the degree to which existing facilities and capabilities, originally created for work on full-scale aircraft, are extensible to smaller scales --those of the small unmanned aerial systems (sUAS, also UAVs and, colloquially, `drones') that have been showing up in the nation's airspace of late. This paper follows an e ort that has led to an initial human{subject psychoacoustic test regarding the annoyance generated by sUAS noise. This e ort spans three phases: 1. The collection of the sounds through field recordings. 2. The formulation and execution of a psychoacoustic test using those recordings. 3. The initial analysis of the data from that test. The data suggests a lack of parity between the noise of the recorded sUAS and that of a set of road vehicles that were also recorded and included in the test, as measured by a set of contemporary noise metrics. Future work, including the possibility of further human subject testing, is discussed in light of this suggestion.
Christian, Andrew↗
Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing
Over the past century aircraft wing design has transformed from the morphing wing used on the Wright Flyer to rigid wings with little to no shape-tailoring abilities. Modern day wings are designed to fly at a single trim condition and optimized to have a maximum aerodynamic efficiency at only this condition. Shape morphing wings on the other hand have the potential to undergo geometric changes allowing them to adapt to their mission profiles. Several flight demonstrations have been conducted over the decades using morphing-wing technologies. Active wing-twist control was demonstrated on the X-53 Active Aeroelastic Wing (AAW) research project by utilizing multiple leading- and trailing-edge control surfaces. Passive morphing technology has been demonstrated on the Rockwell RPRV-870 Highly Maneuverable Aircraft Technology (HiMAT) aircraft. In the current study, the wings of a small unmanned aerial system (sUAS) were modified to have segmented control surfaces (SCS). The modifications include segmenting the original wing control surfaces (one flap and one aileron per wing) into 44 individual sections, each section having its own independent servo control motor. The wings were also instrumented with a network of over 1800 fiber-optic strain sensors (on four sensing fibers distributed over the top and bottom surfaces of the wing) monitoring the strain response of the wing to aerodynamic loading. The SCS positions were manipulated in real time to modify the spanwise lift distribution of the wings on the sUAS. The change in the structural response of the wings caused by load redistribution was quantified by measuring the bending strains on the upper and lower wing surfaces using an on-board compact fiber-optic strain sensing (cFOSS) system. A feedback controller was developed to control the SCS positions using strain-based shape estimations from the Displacement Transfer Function (DTF). Post-processing of the strain data allowed for the transverse displacement distributions and load distributions to be compared for the conventional and segmented control surface cases using displacements and loads algorithms developed by Richards and Ko at AFRC (refs. 5-10). While the current study focused on the shifting of the spanwise aerodynamic loads as quantified by displacements, future applications for loads or displacement control might include active gust alleviation and flutter suppression.
Pena, Francisco↗
Small Unmanned Aircraft Systems Integration into the National Airspace System Visual-Line-of-Sight Human-in-the-Loop Experiment
As part of the Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) project, research on integrating small UAS (sUAS) into the NAS was underway by a human-systems integration (HSI) team at the NASA Langley Research Center. Minimal to no research has been conducted on the safe, effective, and efficient manner in which to integrate these aircraft into the NAS. sUAS are defined as aircraft weighing 55 pounds or less. The objective of this human system integration team was to build a UAS Ground Control Station (GCS) and to develop a research test-bed and database that provides data, proof of concept, and human factors guidelines for GCS operations in the NAS. The objectives of this experiment were to evaluate the effectiveness and safety of flying sUAS in Class D and Class G airspace utilizing manual control inputs and voice radio communications between the pilot, mission control, and air traffic control. The design of the experiment included three sets of GCS display configurations, in addition to a hand-held control unit. The three different display configurations were VLOS, VLOS + Primary Flight Display (PFD), and VLOS + PFD + Moving Map (Map). Test subject pilots had better situation awareness of their vehicle position, altitude, airspeed, location over the ground, and mission track using the Map display configuration. This configuration allowed the pilots to complete the mission objectives with less workload, at the expense of having better situation awareness of other aircraft. The subjects were better able to see other aircraft when using the VLOS display configuration. However, their mission performance, as well as their ability to aviate and navigate, was reduced compared to runs that included the PFD and Map displays.
Trujillo, Anna C.↗
TCL4 UTM (UAS Traffic Management) Nevada 2019 Flight Tests, Airspace Operations Laboratory (AOL) Report
The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, NASA has organized a series of flight test demonstrations. Technology Capability Level-4 (TCL4) flight tests were conducted at a Nevada, USA test site, during June 2019. The testing resulted in over 300 data collection flights using eight live rotorcraft, 15 simulated vehicles, involving six flight crews and five Unmanned Aerial System (UAS) Service Suppliers (USS). The TCL4 approach was designed to demonstrate five scenarios that set up five diverse sets of UAS events and activities. The Nevada test site focused on three of these scenarios: an incoming weather front, a concert event with an incident requiring an emergency response, and a scenario where multiple vehicles experienced Communication, Navigation, and Surveillance (CNS) issues. The test site created their scenarios to each have three phases and were required to complete three executions of each scenario, for a total of nine missions per Nevada vehicle per scenario. This document presents data collected from participants during the TCL4-Nevada flight test that provides information about how much and how well operators were able to make use of UTM functions and information, with the goal of exploring what the minimum information requirements and/or best practices might be in TCL4 operations. The driving enquiry was: how do UTM tools and features support (human) operators leading to safe and effective conduct of large-scale beyond visual line of sight (BVLOS) sUAS operations in “urban canyon” environments? As with the data collected during previous similar tests (e.g., TCL3, Martin et al., 2019), the quality of the UTM information exchanged, and the meaningfulness and therefore usefulness of this information, were all focal points of the questions asked and the data collected. Data aligned with five human-system attributes to indicate that UTM provided information that contributed to users’ ability to operate safely and effectively within UTM, but that information was not always complete and was sometimes unclear.
UTM↗
Airborne Radar for sUAS Sense and Avoid
A primary challenge for the safe integration of small UAS operations into the National Airspace System (NAS) is traffic deconfliction, both from manned and unmanned aircraft. The UAS Traffic Management (UTM) project being conducted at the National Aeronautics and Space Administration (NASA) considers a layered approach to separation provision, ranging from segregation of operations through airspace volumes (geofences) to autonomous sense and avoid (SAA) technologies for higher risk, densely occupied airspace. Cooperative SAA systems, such as Automatic Dependent Surveillance-Broadcast (ADS-B) and/or vehicle-to-vehicle communication systems provide significant additional risk mitigation but they fail to adequately mitigate collision risks for non-cooperative (non-transponder equipped) airborne aircraft. The RAAVIN (Radar on Autonomous Aircraft to Verify ICAROUS Navigation) flight test being conducted by NASA and the Mid-Atlantic Aviation Partnership (MAAP) was designed to investigate the applicability and performance of a prototype, commercially available sUAS radar to detect and track non-cooperative airborne traffic, both manned and unmanned. The radar selected for this research was a Frequency Modulated Continuous Wave (FMCW) radar with 120 degree azimuth and 80 degree elevation field of view operating at 24.55GHz center frequency with a 200 MHz bandwidth. The radar transmits 2 watts of power thru a Metamaterial Electronically Scanning Array antenna in horizontal polarization. When the radar is transmitting, personnel must be at least 1 meter away from the active array to limit nonionizing radiation exposure. The radar physical dimensions are 18.7cm by 12.1cm by 4.1cm and it weighs less than 820 grams making it well suited for installation on small UASs. The onboard, SAA capability, known as ICAROUS, (Independent Configurable Architecture for Reliable Operations of Unmanned Systems), developed by NASA to support sUAS operations, will provide autonomous guidance using the traffic radar tracks from the onboard radar. The RAAVIN set of studies will be conducted in three phases. The first phase included outdoor, ground-based radar evaluations performed at the Virginia Tech’s Kentland Farm testing range in Blacksburg, VA. The test was designed to measure how well the radar could detect and track a small UAS flying in the radar’s field of view. The radar was used to monitor 5 test flights consisting of outbound, inbound and crossing routes at different ranges and altitudes. The UAS flown during the ground test was the Inspire 2, a quad copter weighing less than 4250 grams (10 pounds) at maximum payload. The radar was set up to scan and track targets over its full azimuthal field of view from 0 to 40 degrees in elevation. The radar was configured to eliminate tracks generated from any targets located beyond 2000 meters from the radar and moving at velocities under 1.45 meters per second. For subsequent phases of the study the radar will be integrated with a sUAS platform to evaluate its performance in flight for SAA applications ranging from sUAS to manned GA aircraft detections and tracking. Preliminary data analysis from the first outdoor ground tests showed the radar performed well at tracking the vehicle as it flew outbound and repeatedly maintained a track out to 1000 meters (maximum 1387 meters) until the vehicle slowed to a stop to reverse direction to fly inbound. As the Inspire flew inbound tracks from beyond 800 meters, a reacquisition time delay was consistently observed between when the Inspire exceeds a speed of 1.45 meters per second and when the radar indicated an inbound target was present and maintained its track. The time delay varied between 6 seconds to over 37 seconds for the inbound flights examined, and typically resulted in about a 200 meter closure distance before the Inspire track was maintained. The radar performed well at both acquiring and tracking the vehicle as it flew crossing routes out past 400 meters across the azimuthal field of view. The radar and ICAROUS software will be integrated and flown on a BFD-1400-SE8-E UAS during the next phase of the RAAVIN project. The main goal at the conclusion of this effort is to determine if this radar technology can reliably support minimum requirements for SAA applications of sUAS. In particular, the study will measure the range of vehicle detections, lateral and vertical angular errors, false and missed/late detections, and estimated distance at closest point of approach after an avoidance maneuver is executed. This last metric is directly impacted by sensor performance and indicates its suitability for the task.
Szatkowski, George N.↗
Integration of Advanced Concepts and Vehicles Into the Next Generation Air Transportation System: Introduction, Key Messages, and Vehicle Attributes - Volume 1
Raytheon, in partnership with NASA, is leading the way in ensuring that the future air transportation continues to be a key driver of economic growth and stability and that this system provides an environmentally friendly, safe, and effective means of moving people and goods. A Raytheon-led team of industry and academic experts, under NASA contract NNA08BA47C, looked at the potential issues and impact of introducing four new classes of advanced aircraft into the next generation air transportation system -- known as NextGen. The study will help determine where NASA should further invest in research to support the safe introduction of these new air vehicles. Small uncrewed or unmanned aerial systems (SUAS), super heavy transports (SHT) including hybrid wing body versions (HWB), very light jets (VLJ), and supersonic business jets (SSBJ) are the four classes of aircraft that we studied. Understanding each vehicle's business purpose and strategy is critical to assessing the feasibility of new aircraft operations and their impact on NextGen's architecture. The Raytheon team used scenarios created by aviation experts that depict vehicles in year 2025 operations along with scripts or use cases to understand the issues presented by these new types of vehicles. The information was then mapped into the Joint Planning and Development Office's (JPDO s) Enterprise Architecture to show how the vehicles will fit into NextGen's Concept of Operations. The team also identified significant changes to the JPDO's Integrated Work Plan (IWP) to optimize the NextGen vision for these vehicles. Using a proven enterprise architecture approach and the JPDO s Joint Planning Environment (JPE) web site helped make the leap from architecture to planning efficient, manageable and achievable. Very Light Jets flying into busy hub airports -- Supersonic Business Jets needing to climb and descend rapidly to achieve the necessary altitude Super-heavy cargo planes requiring the shortest common flight path -- are just a few of the potential new operations in the future National Airspace System. To assess the impact of these new scenarios on overall national airspace operations, the Raytheon team used the capabilities of a suite of tools such as NASA's Airspace Concepts Evaluation System (ACES), the Flight Optimization System (FLOPS), FAA's Aviation Environmental Design Tool (AEDT), Intelligent Automations Kinematic Trajectory Generator (KTG) and the Aviation Safety Risk Model (ASRM). Detailed metroplex modeling, surface delay models for super heavy transports, prioritized routing and corridors for supersonics business jets, and VLJ demand models are some of the models developed by the Raytheon team to study the effect of operating these new vehicles in the future NAS. Using this suite of models, several trade studies were conducted to evaluate these effects in terms of delays, equity in access, safety, and the environment. Looking at the impact of each vehicle, a number of critical issues were identified. The Raytheon team concluded that strict compliance to NextGen's 4-dimensional trajectory (4DT) management will be required to accommodate these vehicles unique operations and increased number of flights in the future air space system. The next section provides a discussion of this and the other key findings from our study.
Zellweger, Andres↗
Effects of Autonomous sUAS Separation Methods on Subjective Workload, Situation Awareness, and Trust
The Unmanned Aircraft System (UAS) Traffic Management (UTM) concept was designed to support autonomous small UAS operations at a large-scale and without direct human intervention. However, human-autonomy interactions will be impacted by situation awareness, workload, and trust in the autonomy. Method: Nine participants monitored live small UAS operations in a representative UTM system during a series of traffic conflict scenarios and then provided subjective responses regarding situation awareness, workload, and trust in the autonomous separation method. The study employed a 3 (Separation Method: Autonomous Sense and Avoid, Geofence, Manual) × 2 (Incursion: High, Medium) within subjects design. Results: Situation awareness ratings for both autonomous separation methods were significantly lower than the manual condition. An interaction indicated differential workload ratings for the Autonomous Sense and Avoid separation ratings. Trust ratings significantly dropped when the Geofencing separation method failed. Conclusion: Subjective responses of remote operators in the UTM system are affected by the vehicle separation methods. Operators’ understanding of decisions made by the autonomous systems onboard the vehicle likely influence this effect
UAS↗