Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Abstract

Unmanned aerial systems (UASs) are continuing to proliferate. Quantitative risk assessment for UAS operations, both a priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. In this paper, the problem of UAS risk analysis and decision making is treated through a novel application of Dempster–Shafer (DS) networks using auto-updating transition matrices. This method was motivated by the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of the research detailed in this paper. The paper describes training a DS network based on simulated operation data, testing the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and exploring how this system would extend to a more inclusive UAS ecosystem. Conclusions are drawn with respect to the research performed, and additional research directions are proposed.