Renan G. Maidana, Tarannom Parhizkar, Christoph A. Thieme, Marilia A. Ramos, Ingrid B. Utne, and Ali Mosleh
Centre for Autonomous Marine Operations and Systems (AMOS), Department of Marine Technology, Norwegian University of Science and Technology, Norway.
B. John Garrick Institute for the Risk Sciences, University of California, Los Angeles, United States of America.
Accidents involving maritime vessels can have severe consequences, i.e., high potential loss-of-life and environmental impact. Hence, risk assessment is essential to the safety of a vessel’s operations. Risk assessment for conventional vessels can be considered well established - however, challenges are present for the risk assessment of vessels with autonomous behavior, and generally for complex and software intensive systems. Generally, traditional artificial intelligence methods used in a system to perform tasks autonomously are not risk-informed, which may later result in an accident scenario. For example, state-of-the-art autonomous navigation methods are reactive to risk, acting to avoid hazards or consequences only after identifying a potential accident scenario. By preemptively performing risk assessment and incorporating risk information in the autonomous decision-making process, we can proactively avoid an accident scenario altogether. In this paper, we present a novel framework for simulation-based Dynamic Probabilistic Risk Assessment (DPRA), using the Accident Dynamic Simulator (ADS) as the point of departure.
We describe the concepts, structure, constituent parts of the DPRA framework, and how it will contribute to safer autonomous decision-making in the future.
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