Estimating See and Be Seen Performance with an Airborne Visual
  Acquisition Model
Ngaire Underhill Evan Maki Bilal Gill Andrew Weinert
Separation provision and collision avoidance to avoid other air traffic are
fundamental components of the layered conflict management system to ensure safe
and efficient operations. Pilots have visual-based separation responsibilities
to see and be seen to maintain separation between aircraft. To safely integrate
into the airspace, drones should be required to have a minimum level of
performance based on the safety achieved as baselined by crewed aircraft seen
and be seen interactions. Drone interactions with crewed aircraft should not be
more hazardous than interactions between traditional aviation aircraft.
Accordingly, there is need for a methodology to design and evaluate detect and
avoid systems, to be equipped by drones to mitigate the risk of a midair
collision, where the methodology explicitly addresses, both semantically and
mathematically, the appropriate operating rules associated with see and be
seen. In response, we simulated how onboard pilots safely operate through see
and be seen interactions using an updated visual acquisition model that was
originally developed by J.W. Andrews decades ago. Monte Carlo simulations were
representative two aircraft flying under visual flight rules and results were
analyzed with respect to drone detect and avoid performance standards.