Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles (2024)

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Author: Murat Kirişci

Published: 25 June 2024 Publication History

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    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 circ*mstances. 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.

    Highlights

    A hybrid technique based on IVFF-AHP,-TOPSIS, -MABAC procedures has been suggested.

    Interval-valued Fermatean fuzzy sets were used for the new method.

    IVFF-AHP calculates the weights and the hazards are ranked by IVFF-TOPSIS,-MABAC.

    A sensitivity analysis was performed to confirm the result of the new methodology.

    The effectiveness of the new hybrid approach was evaluated by comparison analysis.

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    Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles (1)

    Applied Soft Computing Volume 152, Issue C

    Feb 2024

    1017 pages

    ISSN:1568-4946

    Issue’s Table of Contents

    Elsevier B.V.

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    Elsevier Science Publishers B. V.

    Netherlands

    Publication History

    Published: 25 June 2024

    Author Tags

    1. Self-driving vehicles
    2. Risk assessment
    3. Fermatean fuzzy environment
    4. AHP
    5. TOPSIS
    6. MABAC
    7. Multi-Criteria Decision-Making(MCDM)

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