Airspace Risk Management for UAVs A Framework for Optimising Detector Performance Standards and Airspace Traffic using JARUS SORA

Abstract

The Joint Authority for Rulemaking on UAS (JARUS) recently released a process for managing air and ground risk for Unmanned Aerial Vehicle (UAV) operations: the Specific Operations Risk Assessment (SORA) [1]. This paper focuses on the air risk element, where the challenge of balancing equipment performance for detect and avoid functions against the likelihood they will be needed (encounter rates) is further complicated by safety and costs implications. To date, attempts to achieve this balance have largely been conducted using qualitative measures. The problem with this approach is that it risks superimposing unnecessary cost burdens on industry without transparent evidence it is delivering the perceived safety benefit, or conversely, the process results in under-estimates for encounter rates with inadequate performance standards stipulated. To address this issue, we introduce a Bayesian framework that explicitly links encounter rate exposure, detection performance, cost and safety. We then detail how the framework can be deployed to appropriately match airspace characteristics with suitable equipment performance levels, whilst optimising safety and cost. As part of our testing regime, we identified that a mis-representation of actual traffic encounter rates creates compounding implications for detector performance standards and safety. Accordingly, we incorporate our efforts to characterise a region of Australian airspace, and contrast it with a the qualitative characterisation methods employed within the SORA.