A Bayesian Network Augmentation of JARUS SORA 2.5 for Probabilistic UAS Operational Risk Assessment: A Proof-of-Concept Study

This proof-of-concept study examines whether a Bayesian Network (BN) can augment the evidentiary layer of the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) Specific Operations Risk Assessment (SORA) 2.5. The architecture retains the official Final Ground Risk Class (GRC), Residual Air Risk Class (ARC), and Specific Assurance and Integrity Level (SAIL) state spaces and uses a deterministic SAIL mapping, while uncertain operational evidence is represented probabilistically. A VLOS airframe-inspection scenario at Riga Airport and a deliberately adverse stress scenario are used to illustrate model behavior. Under the assumptions encoded in the submitted model, the modal states for the airport scenario coincide with the conventional SORA classifications (GRC 4, ARC c, and SAIL IV), and one-way sensitivity identifies aircraft size classification, ground-risk mitigation effectiveness, and airspace complexity as the strongest model drivers. These numerical posteriors are assumption-dependent outputs of uncalibrated engineering judgements, not observed frequencies, validation evidence, or probabilities of regulatory approval. The present contribution is therefore limited to demonstrating feasibility and sensitivity analysis value; empirical calibration, structured elicitation, multiple operational cases, and release of the executable model and complete parameter tables are required for independent replication and practical use.