Develop a Data Driven Framework to Inform Safety Risk Management Mitigation Credit Estimates (A82_A11L.UAS.112)
Safety Risk Management Panels (SRMPs) currently do not have an objective way in which to evaluate the likelihood credit given to a proposed risk mitigation, or a combination of risk mitigations, that are proposed for different types of UAS operations. This typically leads to the use of a more subjective evaluation process, which can lead to inconsistent estimates – both overestimates and underestimates – of likelihood credits. SRM panels often resort to subjective evaluations for the amount of likelihood credit to grant each mitigation proposed in an operation. The inconsistency of these estimates requires research to provide data driven estimates for at least the commonly proposed mitigations. The goal of this project is to perform the research necessary to establish data driven estimates of likelihood credits for commonly proposed risk mitigations in the UAS domain, which will ultimately lead to the development of a more objective methodology for performing the likelihood credit evaluation process for proposed risk mitigations for UAS operations by SRMPs.
POC:
Andrew Shepherd
Email: andrew.shepherd@sinclair.edu
Phone: 937.512.5751
Participating Schools
