Abstract
When solving the problems of automotive technical expertise in road traffic accidents (RTAs), decisions are made in conditions of incomplete information, that is, in conditions of uncertainty. In the decision-making process, different types of uncertainty arise, depending on the reasons for its occurrence: quantitative, informational, cost-based, professional, restrictive, and the external environment. In addition, uncertainty can be stochastic or fuzzy. The lack of a unified methodological approach to assessing and minimizing the impact of uncertainty on the results of an autotechnical examination of an accident can lead to a significant error in determining the parameters under study. The aim of the work is to generalize and develop existing approaches to assessing the uncertainty of methodological support of automotive technical expertise and minimize the subjectivity of the formation of expert conclusions in the process of establishing the circumstances of emergencies. The structure of the formation of uncertainty and methods for its assessment in solving the problems of automotive technical expertise of accidents are considered. It is shown that the decision-making process in the automotive technical expertise of road accidents should be considered not only as deterministic, but also as a stochastic and fuzzy process that requires the use of the synthesis of deterministic, probabilistic, regression and neuro-fuzzy models to take into account most of the factors that influence to reduce uncertainty in the formation of expert opinions. It is proposed to evaluate the uncertainty of the methodological support of the automotive technical expertise of traffic accidents by the indicators of generalized informational entropy, which is not a property of the adopted system of automotive technical expertise of traffic accidents, but depends on the way this system is described. A method of normalized entropy is developed, which, unlike the existing ones, is a universal tool for assessing compositional uncertainty (composition of stochastic and fuzzy uncertainty), characteristic for this type of problem. It is shown that taking stochastic and fuzzy uncertainties into account allows us to narrow the range of possible solutions when conducting an examination by 20%, and compared with the deterministic approach, the subjectivity of forming expert conclusions in establishing the circumstances of emergencies decreases by 46-48%.
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