Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles

Abstract

The decision-making(DM) processes used by autonomous vehicle driving systems are separate from those of the users, allowing them to oversee and regulate the operations of the cars in expected and unforeseen circumstances. Although there are several advantages to using this technology, such as fewer accidents caused by human error and more efficient energy utilization, it is also evident that there are some risks involved. Hence, developing a risk assessment application for these systems will be advantageous given the hazards associated with autonomous cars and driving systems that must be tested and addressed. In this study, a new integrated FF-based MCDM methodology combining the Analytic Hierarchy Process(AHP), Technique for Order Preference by Similarity to Ideal Solution(TOPSIS), and Multi-Attributive Border Approximation Area Comparison (MABAC) methods is proposed as a new security model that will help decision-makers address the physical design and attack risks of autonomous vehicles, estimate their uncertainty, and control cyber risks Interval-valued Fuzzy Fermatean sets ten possibilities for autonomous vehicle driving systems assessed in the application based on six main criteria and fifteen sub-criteria. Comparative and sensitivity studies have also been used to demonstrate the adaptability, validity, and verification of the suggested approach and the sensitivity of the decisions made. Possible implications from a theoretical, managerial, and policy framework have been examined based on the application findings and studies that have been done.