A Decomposed Fuzzy Analytical Hierarchy Process to Assess the Risks of Autonomous Vehicles

Intelligent Transportation System (ITS) is a new field that integrates transportation management and control, infrastructure, operations, and policies. ITS has focused on autonomous vehicles (AVs) to eliminate the need for drivers, promote traffic control, reduce transportation and infrastructure expenses, and enhance customer convenience in recent years. However, AVs pose different uncertainties and risks due to their advanced technology, performance under diverse road conditions, and increased vulnerability to security threats. Fuzzy set theory is considered an appropriate method for evaluating AV risks that encompass uncertainties. Decomposed fuzzy sets (DFS), which is recently proposed, are a useful theory that involves posing positive and negative inquiries to assist decision-makers in providing more efficient and consistent responses. In this study, the main risk factors of AVs have been determined. The most and least critical risk factors of Avs have been found based on decomposed fuzzy analytic hierarchy process enhances the safety of outcomes when used with DFS, primarily relying on the pairwise comparison technique. A comparison of techniques was conducted utilizing the classical Analytic Hierarchy Process (AHP) approach. The defined order of risks is considered as beneficial for policy makers, politicians, vehicle technology manufacturers, and other stakeholders.