Results for “NMAC”

NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗

NASA NTRS2018

Differential Adaptive Stress Testing of Airborne Collision Avoidance Systems

The next-generation Airborne Collision Avoidance System (ACAS X) is currently being developed and tested to replace the Traffic Alert and Collision Avoidance System (TCAS) as the next international standard for collision avoidance. To validate the safety of the system, stress testing in simulation is one of several approaches for analyzing near mid-air collisions (NMACs). Understanding how NMACs can occur is important for characterizing risk and informingdevelopment of the system. Recently, adaptive stress testing (AST) has been proposed as a way to find the most likely path to a failure event. The simulation-based approach accelerates search by formulating stress testing as a sequential decision process then optimizing it using reinforcement learning. The approach has been successfully applied to stress test a prototype of ACAS Xin various simulated aircraft encounters. In some applications, we are not as interestedin the system's absolute performance as its performance relative to another system. Such situations arise, for example, during regression testing or when deciding whether a new system should replace an existing system. In our collision avoidance application, we are interested in finding cases where ACAS X fails but TCAS succeeds in resolving a conflict. Existing approaches do not provide an efficient means to perform this type of analysis. This paper extends the AST approach to differential analysis by searching two simulators simultaneously and maximizing the difference between their outcomes. We call this approach differential adaptive stress testing (DAST). We apply DAST to compare a prototype of ACAS X against TCAS and show examples of encounters found by the algorithm.

Lee, Ritchie↗

NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗

NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗

NASA NTRS2019

Alternative Surveillance Fast Time Simulation with Sensor Uncertainties and Mitigation

This presentation describes a simulation plan for the fast time simulation 3 for the modeling and simulation team in the UAS (Unmanned Aircraft Systems) in the NAS (National Airspace System)project. Objectives: Investigate the effect of limited surveillance volume and realistic radar noise on DAA (Detect and Avoid) performance; Evaluate the ability of the sensor uncertainty mitigation (SUM) feature of the DAIDALUS [Dectect and Avoid...] algorithm to improve DAA alerting and guidance [Includes content on SWaP-C (size, weight, power, and cost) of unmanned aircraft; and Near-mid-air-collision (NMAC).]

UAS↗

NASA NTRSDate not supplied

UAS Pilot Performance Comparisons with Different Low Size, Weight and Power Sensor Ranges

The present study evaluated the performance of UAS pilots under four simulated low size, weight, and power (SwaP) sensor ranges: 1.5nmi, 2.0nmi, 2.5nmi, and 3.0nmi. Nine active-duty UAS pilots responded to scripted DAA conflicts against non-cooperative intruders while flying a simulated RQ-7 Shadow at varied speeds along a pre-filed flight path in Class E airspace. Findings revealed a linear effect of sensor range on alerting time and separation performance, with nearly every DAA well clear (DWC) violation and all Near Mid-Air Collision (NMAC) events occurring below 2.5nmi. Response time differences at these reduced ranges were negligible due to the high frequency of warning-level alerts that require an immediate response. Since caution alert duration was truncated to some degree by each tested declaration range, pilots were often unable to coordinate their avoidance maneuvers with ATC prior to their uploads. Nonetheless, the 2.5nmi range allowed minimum alerting times that were sufficient for acceptable pilot performance. The study findings will inform DAA system requirements for UAS with alternative surveillance equipment and aircraft performance capabilities. Implications on DAA display and sensor requirements are discussed.

unmanned aircraft systems↗

NASA NTRSDate not supplied

UAS Pilot Performance Comparisons with Different Low Size, Weight and Power Sensor Ranges

The present study evaluated the performance of UAS pilots under four simulated low size, weight, and power (SwaP) sensor ranges: 1.5nmi, 2.0nmi, 2.5nmi, and 3.0nmi. Nine active-duty UAS pilots responded to scripted DAA conflicts against non-cooperative intruders while flying a simulated RQ-7 Shadow at varied speeds along a pre-filed flight path in Class E airspace. Findings revealed a linear effect of sensor range on alerting time and separation performance, with nearly every DAA well clear (DWC) violation and all Near Mid-Air Collision (NMAC) events occurring below 2.5nmi. Response time differences at these reduced ranges were negligible due to the high frequency of warning-level alerts that require an immediate response. Since caution alert duration was truncated to some degree by each tested declaration range, pilots were often unable to coordinate their avoidance maneuvers with ATC prior to their uploads. Nonetheless, the 2.5nmi range allowed minimum alerting times that were sufficient for acceptable pilot performance. These findings will inform DAA system requirements for UAS with alternative surveillance equipment and aircraft performance capabilities. Implications on DAA display and sensor requirements are discussed.

unmanned aircraft systems↗

NASA NTRS2020

UAS Pilot Performance Comparisons with Different Low Size, Weight and Power Sensor Ranges

The present study evaluated the performance of UAS pilots under four simulated low size, weight, and power (SwaP) sensor ranges: 1.5nmi, 2.0nmi, 2.5nmi, and 3.0nmi. Nine active-duty UAS pilots responded to scripted DAA conflicts against non-cooperative intruders while flying a simulated RQ-7 Shadow at varied speeds along a pre-filed flight path in Class E airspace. Findings revealed a linear effect of sensor range on alerting time and separation performance, with nearly every DAA well clear (DWC) violation and all Near Mid-Air Collision (NMAC) events occurring below 2.5nmi. Response time differences at these reduced ranges were negligible due to the high frequency of warning-level alerts that require an immediate response. Since caution alert duration was truncated to some degree by each tested declaration range, pilots were often unable to coordinate their avoidance maneuvers with ATC prior to their uploads. Nonetheless, the 2.5nmi range allowed minimum alerting times that were sufficient for acceptable pilot performance. These findings will inform DAA system requirements for UAS with alternative surveillance equipment and aircraft performance capabilities. Implications on DAA display and sensor requirements are discussed.

unmanned aircraft systems↗

NASA NTRSDate not supplied

Advanced Air Mobility Operations & Automation Part II Technical Lecture

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current presentation discusses two human-in-the-loop simulations that presented rotorcraft pilots with ACAS XR alerts and guidance in eVTOL simulators with varying levels of automation. It also presents overviews of these studies as well as how they assisted live flight tests, which will occur throughout 2023.

air taxis↗