Evaluating the effect of risk metrics for supporting operational decision-making by autonomous surface vehicles

Risk models may provide information about the risk associated with the operation of technical systems, and their output may be used as input to decision-making, e.g., by human operators. For highly autonomous systems, it is desirable to use risk models to enable the system itself to make risk-aware decisions. This impacts the requirements to the output of the risk model, as it must be directly useful for the decisions made by a control system. This study investigates how risk metrics can be used for supporting decisions by an autonomous surface vehicle (ASV) following an autonomous underwater vehicle. The feasibility of three different risk metrics is evaluated: expected economic loss, individual risk, and hazardous event probability. The metrics are evaluated in an analysis focused on ASV path planning. Both simulations, and experimental field trials with a real ASV in the Trondheimsfjord, Norway, were performed to investigate if and how the choice of risk metric affects the decisions made by the ASV. The results show the influence of each metric on the ASV’s planned path and illustrate the importance of considering the potential impact of the risk metric on safety when designing control algorithms and supervisory risk controllers for autonomous systems.