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Predicting the Validity of Expert Judgments in Assessing the Impact of Risk Mitigation Through Failure Prevention and Correction

机译：通过预防和纠正来预测评估风险减缓影响的专家判决的有效性

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Operational risk management of autonomous vehicles in extreme environments is heavily dependent on expert judgments and, in particular, judgments of the likelihood that a failure mitigation action, via correction and prevention, will annul the consequences of a specific fault. However, extant research has not examined the reliability of experts in estimating the probability of failure mitigation. For systems operations in extreme environments, the probability of failure mitigation is taken as a proxy of the probability of a fault not reoccurring. Usinga prioriexpert judgments for an autonomous underwater vehicle mission in the Arctic anda posteriorimission field data, we subsequently developed a generalized linear model that enabled us to investigate this relationship. We found that the probability of failure mitigation alone cannot be used as a proxy for the probability of fault not reoccurring. We conclude that it is also essential to include the effort to implement the failure mitigation when estimating the probability of fault not reoccurring. The effort is the time taken by a person (measured in person-months) to execute the task required to implement the fault correction action. We show that once a modicum of operational data is obtained, it is possible to define a generalized linear logistic model to estimate the probability a fault not reoccurring. We discuss how our findings are important to all autonomous vehicle operations and how similar operations can benefit from revising expert judgments of risk mitigation to take account of the effort required to reduce key risks.
机译：极端环境中自治车辆的操作风险管理严重依赖于专家判决，特别是判断失败缓解行动，通过纠正和预防的可能性会为特定断层的后果。然而，现存的研究没有检查专家的可靠性，估计失败减缓的可能性。对于极端环境中的系统操作，失败缓解的概率被视为故障不再重复的概率的代理。在北极和后后视场数据中使用Priatexpert判断，用于北极和地区数据的自主水下车辆任务，我们随后开发了一种通用的线性模型，使我们能够调查这种关系。我们发现单独的失败减缓的概率不能用作故障不再转移的概率的代理。我们得出结论，在估计故障不再转移的概率时，还必须努力实施失败缓解。努力是人员（在人数上测量）执行执行故障校正动作所需的任务所需的时间。我们表明，一旦获得了运行数据的型号，就可以定义广义的线性逻辑模型，以估计故障不再转换的概率。我们讨论我们的研究结果如何对所有自主车辆运营以及如何从修改风险缓解的专家判断中受益的措施，以考虑减少关键风险所需的努力。

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