A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments
The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily de-pendent on expert subjective judgment. Expert judgments can be elicited eithermathematical...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.653.7341 http://eprints.soton.ac.uk/342034/1/jtech-d-12-00005.1.pdf |
| Summary: | The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily de-pendent on expert subjective judgment. Expert judgments can be elicited eithermathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes ’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigationmeasures. Applying this approach to anAUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9 % and that the experts ’ assessments of the effectiveness of risk mitigation led to a maximum of 24 % in risk reduction. |
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