Weinert, A.; Alvarez, L.; Owen, M. and Zintak, B. Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk. Journal of Air Transportation, 2022, pp.1-12.
Weinert, A.; Alvarez, L.; Owen, M. and Zintak, B. Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk. Journal of Air Transportation, 2022, pp.1-12.
Cite as:
Weinert, A.; Alvarez, L.; Owen, M. and Zintak, B. Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk. Journal of Air Transportation, 2022, pp.1-12.
Weinert, A.; Alvarez, L.; Owen, M. and Zintak, B. Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk. Journal of Air Transportation, 2022, pp.1-12.
Abstract
The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations in the National Airspace System. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft are based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision (NMAC). Since midair collisions are difficult to observe in simulation and are inherently rare events, basing evaluations on NMAC enables a more robust statistical analysis. However, an NMAC and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller UAS-only encounters. The primary contribution of this paper is to explore quantitative criteria to use when simulating two or more smaller UASs in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assumes a historically motivated upper bound for the collision likelihood and subsequently identify the smallest possible NMAC analogs. We also demonstrate the NMAC analogs can be used to support modeling and simulation activities.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
