AI-Based Economic Models for Evaluating Vehicle Safety Costs and Benefits
Abstract
This research focuses on artificial intelligent efficient models for the analysis of risks and asset values of vehicles applying globalization, feature reduction, and time series methods. The research concerns the increasing pressure to evaluate economic effects of protective features in a constantly changing car environment. Normalization normalizes disparate data, creating a common framework on which to evaluate cost and benefit variables. Defined subspaces eliminate noise and unnecessary data by outlining the strength of predominant features thus enabling a reduction of computational load and data models. The countless models of time series look at the past and present data to provide future long-standing trends in safety paying as well as the economic consequences. The given framework provides a clear understanding of evaluating the cost-effectiveness of the proposed measures, including the rates of accident reduction, insurance cost, and the costs of adoptive technologies.