Estimating See and Be Seen Performance with an Airborne Visual Acquisition Model

• URL: http://arxiv.org/abs/2307.05502v1
• Date: Thu, 29 Jun 2023 11:39:10 GMT
• Title: Estimating See and Be Seen Performance with an Airborne Visual Acquisition Model
• Authors: Ngaire Underhill and Evan Maki and Bilal Gill and Andrew Weinert
• Abstract summary: Separation provision and collision avoidance are fundamental components of layered conflict management system. Pilots have visual-based separation responsibilities to see and be seen to maintain separation between aircraft. Drone interactions with crewed aircraft should not be more hazardous than interactions between traditional aviation aircraft.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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