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Results for “ACAS-Xu”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Comparative Analysis of ACAS-Xu and DAIDALUS Detect-and-Avoid Systems

The Detect and Avoid (DAA) capability of a recent version (Run 3) of the Airborne Collision Avoidance System-Xu (ACAS-Xu) is measured against that of the Detect and AvoID Alerting Logic for Unmanned Systems (DAIDALUS), a reference algorithm for the Phase 1 Minimum Operational Performance Standards (MOPS) for DAA. This comparative analysis of the two systems' alerting and horizontal guidance outcomes is conducted through the lens of the Detect and Avoid mission using flight data of scripted encounters from a recent flight test. Results indicate comparable timelines and outcomes between ACAS-Xu's Remain Well Clear alert and guidance and DAIDALUS's corrective alert and guidance, although ACAS-Xu's guidance appears to be more conservative. ACAS-Xu's Collision Avoidance alert and guidance occurs later than DAIDALUS's warning alert and guidance, and overlaps with DAIDALUS's timeline of maneuver to remain Well Clear. Interesting discrepancies between ACAS-Xu's directive guidance and DAIDALUS's "Regain Well Clear" guidance occur in some scenarios.

Davies, Jason T.↗
NASA NTRS2015

ACAS-Xu Initial Self-Separation Flight Tests

The purpose of this flight test report is to document and report the details of the ACAS Xu (Airborne Collision Avoidance System For Unmanned Aircraft) / Self-Separation flight test series performed at Edwards AFB from November to December of 2014. Included in this document are details about participating aircraft, aircrew, mission crew, system configurations, flight data, flight execution, flight summary, test results, and lessons learned.

UAS in the NAS↗
NASA NTRS2019

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: ACAS-Xu Run 5 Human-In-The-Loop Sim SC-147 Results Outbrief

This presentation provides an overview of a recently completed human-in-the-loop simulation, conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. This study examined how to present resolution advisories (RAs) issued by Airborne Collision Avoidance System (ACAS) Xu, which can be in the vertical dimension, the horizontal dimension, or both (i.e., "blended"). The study varied the location of the ACAS Xu traffic information - it was either presented within an 'integrated' display or a 'standalone' display, where the traffic information was separated from the vehicle control interfaces and navigational information. Results revealed generally positive feedback on the visual and aural presentation of RAs, with special considerations and performance implications noted throughout.

Monk, Kevin J.↗
NASA NTRS2020

ACAS-Xu Run 5 HITL (June 2019) SC-228 WG 1.3 Results Outbrief

This presentation provides an overview of a recently completed human-in-the-loop simulation, conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. This study examined how to present resolution advisories (RAs) issued by Airborne Collision Avoidance System (ACAS) Xu, which can be in the vertical dimension, the horizontal dimension, or both (i.e., “blended”). The study varied the location of the ACAS Xu traffic information - it was either presented within an 'integrated' display or a 'standalone' display, where the traffic information was separated from the vehicle control interfaces and navigational information. Results revealed generally positive feedback on the visual and aural presentation of RAs, with special considerations and performance implications noted throughout.

Monk, Kevin J.↗
NASA NTRSDate not supplied

DO 365 Test Procedure Results and Analysis Discussion

CAL will leverage M&S tools and existing encounter data to evaluate the robustness of the DAA MOPS Test Procedures. Recently, the SC-147 ACAS-Xu team performed an analysis evaluating the Xu implementation against the DAA MOPS test procedures. Several areas of non-compliance were raised, some indicating shortfalls in our DAA test procedures and/or requirements. The ACAS-Xu team is expected to put out a report highlighting these exemptions. This work will build off that ACAS-Xu analysis/report and perform a full characterization of the statistical test procedures and document the findings. Additionally, CAL will provide recommendations for addressing these findings, potentially resulting in changes to test procedures and/or requirements.

ACAS-Xu↗
NASA NTRSDate not supplied

On the Probabilistic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

neural nets↗