Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. During operations, an unmanned aircraft system should attempt to remain "well clear" to minimize the need for a collision avoidance action. Previously, a well-clear definition was adopted for large unmanned aircraft systems; however, this definition is not appropriate for small unmanned aircraft system weighing less than 55 lb operating at low altitudes. In response, this paper outlines research toward a definition of well clear for small unmanned aircraft systems, based on airborne collision risk, for midterm concepts of operations at low altitudes in nonterminal airspace.

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This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.This document is derived from work done for the FAA (and possibly others), it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone's use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein

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This paper outlines an architecture that provides data and software services to enable a set of Unmanned Aircraft (UA) platforms to operate in a wide range of air domains which may include terminal, en route, oceanic and tactical. The architecture allows a collection of command, control, situational awareness, conflict detection and avoidance, and data management elements to be composed in order to meet different requirement sets as defined by specific UA plat-

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain.