NASA NTRS · 20205004531
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Cesar A. Munoz, Aaron Dutle, Anthony Narkawicz. DAIDALUS v2. https://ntrs.nasa.gov/citations/20205004531
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The DAIDALUS Detect and Avoid Algorithm [1] was developed to address the operational needs of Unmanned Aerial Systems (UAS) and meet the Minimum Operational Performance Standards for Detect and Avoid [2]. The DANTi (Detect and Avoid iN the cockpit) concept [3], developed at the National Aeronautics and Space Administration (NASA) Langley Research Center, leverages advancements achieved in surveillance and Detect and Avoid technologies for unmanned aircraft systems as a safety enhancing capability for pilots of manned aircraft. Pilots operating under Visual Flight Rules and not receiving Air Traffic Control radar services rely on see and avoid to remain well clear of other aircraft and avoid collisions. The DANTi concept has been conceived as a safety enhancement capability to remain well clear and avoid potential collisions. The DANTi concept uses a traffic display to provide situational awareness, conflict detection, alerting, and guidance to remain well clear.
This presentation provides an overview of the results from a two-part engineering analysis conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. Part one of this analysis 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 appearance of the alerting and the verbiage used in the aural alerts. Results revealed high acceptability ratings for the appearance of the RA guidance. Part two of the analysis assessed pilot acceptability of varying levels of automation in the collision avoidance (CA) task. Pilots experienced conditions in which ACAS Xu RAs were executed either manually or automatically (i.e., auto-Collision Avoidance). In some conditions the return to a previously filed course was also executed automatically (auto-Return to Course). Results indicated that improvements are needed to how the state of the automation is presented. Implications of these findings are also discussed.
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