Online risk modeling of autonomous marine systems: Case study of autonomous operations under sea ice

Abstract

Since few or no human operators are directly involved in the operation of an autonomous marine system (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision making. The current study combines the system-theoretic process analysis (STPA) with Bayesian belief networks (BBNs) to develop online risk models for an AMS. Furthermore, fuzzy discretization is introduced to deal with evidence uncertainty. The proposed risk model can update the risk level as the operating conditions change, providing a basis for AMS supervisory risk control (SRC). A two-level SRC is proposed in this study. Using the operation of an autonomous underwater vehicle (AUV) under sea ice as an example, the current work presents an online risk model and a corresponding SRC system, focusing on the navigation hazards to the AUV and its potential loss. The results of simulation studies show that the model enables the AUV to be informed of the risk level and to make risk-based decisions accordingly, thereby improving its intelligence. The importance of the evidence uncertainty in online risk models and SRC is analyzed and discussed. The results and conclusions of this analysis can be adapted to other AMSs.