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A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH

Year 2023, Volume: 22 Issue: 44, 292 - 309, 31.12.2023
https://doi.org/10.55071/ticaretfbd.1300893

Abstract

The autonomous vehicle driving systems' decision-making processes are distinct from those of the users, enabling them to supervise and control the operations of automobiles in both anticipated and unforeseen situations. Although utilizing this technology has several benefits, including fewer accidents brought on by human error and more effective energy usage, it is also clear that there are significant risks associated. Therefore, it will be useful to design a risk assessment application for these systems given the risks connected with autonomous vehicles and/or driving systems that must be assessed and addressed. This article presents a multi-criteria decision-making strategy to evaluate the risk probabilities of autonomous vehicle driving systems by combining the AHP technique with interval-valued Fermatean fuzzy sets. Interval-valued Fuzzy Fermat presents six options for autonomous driving systems for vehicles, which have been evaluated in the application based on six main criteria and fifteen sub-criteria criteria. The findings of this study have demonstrated that the threat posed by cyberattacks is being addressed and given priority to improve the success of the introduction of autonomous vehicle driving systems.

References

  • Abdel-Basset. M., Mohamed, M., Zhou, Y. & Hezam, I. (2017). Multi-criteria group decision-making based on the neutrosophic analytic hierarchy process. Journal of Intelligent and Fuzzy Systems, 333(6), 4055-4066.
  • Alkan N. & Kahraman C. (2023). Prioritization of Supply chain digital transformation strategies using multi-expert fermatean fuzzy analytic hierarchy process, Informatica, 34(1), 1-33.
  • Atanassov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96.
  • Boltürk E. & Kahraman C. (2018). A novel interval-valued neutrosophic AHP with cosine similarity measure. Soft Computing, 22(15), 4941-4958.
  • Buckley, J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247.
  • Büyüközkan, G. & Göçer, F. (2021). A novel approach integrating AHP and COPRAS under Pythagorean fuzzy sets for digital supply Chain partner selection. IEEE Transactions On Engineering Management, 68, 1486-1503.
  • Chang, D. (1986). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
  • Garg, H., Shahzadi, G. & Akram M. (2020). Decision-making analysis based on fermatean fuzzy yager aggregation operators with application in COVID-19 testing facility. Mathematical Problems in Engineering, 2020, Article ID 7279027, https://doi.org/10.1155/2020/7279027.
  • Garg, H., Ali, Z. & Mahmood, T. (2021). Algorithms for complex interval-valued q-rung orthopair fuzzy sets in decision-making based on aggregation operators, AHP, and TOPSIS. Expert Systems, 38(1), 1-36.
  • Gül, M. (2018). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and coloring unit. International Journal of Occupational Safety and Ergonomics, 26(4), 705-718.
  • Gündoğdu, F.K., Duleba, S., Moslem, S. & Aydın S. (2021). Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Applied Soft Computing, 100, 106920.
  • ISO 26262 - Parts [2-8] Requirements decomposition concerning ASIL tailoring (2011).
  • Jeevaraj, S. (2021). Ordering of interval-valued Fermatean fuzzy sets and its applications. Expert Systems with Applications, 185, 115613.
  • Kahraman, C., Oztaysi, B., Sari, I. & Turanoglu, E. (2016). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowl Based System, 59, 48-57.
  • Karasan, A., Ilbahar, E. & Kahraman, C. (2019). A novel Pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Computing, 23(21), 10953-10968.
  • Kirişci, M., Demir, I. & Simsek, N. (2022). Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection. Artificial Intelligence in Medicine, 127, 102278. https://doi.org/10.1016/j.artmed.2022.102278.
  • Kirişci, M. (2022a). Correlation Coefficients of Fermatean Fuzzy Sets with Their Application, J. Math. Sci. Model., 5(2), 16-23. https://doi.org/10.33187/jmsm.1039613.
  • Kirisci, M. (2022b). Data Analysis for Lung Cancer: Fermatean Hesitant Fuzzy Sets Approach, Applied Mathematics, Modeling and Computer Simulation, 30, 701-710. https://doi.org/10.3233/ATDE221087.
  • Kirişci, M. (2023). New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach. Knowl Inf Syst, 65, 855–868. https://doi.org/10.1007/s10115-022-01776-4.
  • Mary, F.R.P., Mohanaselvi S. & Broumi S. (2023). A solution approach to minimum spanning tree problem under Fermatean fuzzy environment. Bulletin of Electrical Engineering and Informatics, 12(3), 1738-1746.
  • Mathew, M., Chakrabortty, R. & Ryan, M. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988.
  • Öztaysi, B., Onar S., Boltürk E. & Kahraman C. (2015). Hesitant fuzzy analytic hierarchy process. 2015 IEEE International Conference Fuzzy Systems (FUZZ-IEEE), 2015, 1–7.
  • Saaty, T.L. (2008). The analytic hierarchy and analytic network measurement processes: Applications to decisions under Risk. European Journal of Pure and Applied Mathematics, 1(1), 122-196.
  • Sadiq, R. & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 23(1), 75–91.
  • Senapati, T. & Yager, R.R. (2019a). Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica, 30(2), 391-412.
  • Senapati, T. & Yager, R.R. (2019b). Fermatean fuzzy weighted averaging/geometric operators and their application in multi-criteria decision-making methods. Engineering Applications of Artificial Intelligence, 85, 112-121.
  • Senapati, T. & Yager, R.R. (2020). Fermatean Fuzzy Sets. J. Ambient Intell. Hum. Comp. 11, 663-674.
  • Son, T.D., Bhave, A. & der Auweraer, V. (2019). Simulation-based testing framework for autonomous driving development. IEEE International Conference on Mechatronics, 576-583. https://doi.org/10.1109/ICMECH.2019.8722847.
  • Van Laarhoven, P. & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst, 11(1–3), 229-241.
  • Wu, J., Huang, H. & Cao, Q. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multicriteria decision-making problems. Applied Mathematical Modelling, 37(24), 9898-9906.
  • Yager, R.R. (2013). Pythagorean fuzzy subsets. Proc. Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada.
  • Yager, R.R. (2014). Pythagorean membership grades in multi-criteria decision-making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965.
  • Zadeh, L.A. (1965). Fuzzy sets. Inf. Comp., 8, 338-353.

OTONOM ARAÇ SÜRÜŞ SİSTEMLERİ İÇİN YENİ BİR RİSK DEĞERLENDİRME YÖNTEMİ: FERMATEAN FUZZY AHP YÖNTEMİ

Year 2023, Volume: 22 Issue: 44, 292 - 309, 31.12.2023
https://doi.org/10.55071/ticaretfbd.1300893

Abstract

Otonom araç sürüş sistemlerinin karar verme süreçleri, kullanıcılarınkinden farklıdır ve hem öngörülen hem de öngörülemeyen durumlarda otomobillerin işleyişini denetleme ve kontrol etme olanağı sağlar. Bu teknolojiyi kullanmanın insan hatasından kaynaklanan daha az kaza ve daha verimli enerji kullanımı gibi bir dizi faydası olsa da, bununla ilgili önemli riskler olduğu da açıktır. Bu nedenle, değerlendirilmesi ve ele alınması gereken otonom araçlar ve/veya sürüş sistemleri ile bağlantılı riskler göz önüne alındığında, bu sistemler için bir risk değerlendirme uygulaması tasarlamak faydalı olacaktır. Bu makalede, AHP tekniğini aralık değerli Fermatean bulanık kümelerle birleştirerek otonom araç sürüş sistemlerinin risk olasılıklarını değerlendirileceği çok kriterli bir karar verme stratejisi sunulmaktadır. Aralık değerli Fermatean bulanık kümeler, uygulamada altı ana kriter ve on beş destekleyici kriter temelinde değerlendirilen araçlar için otonom sürüş sistemleri için altı seçenek sunar. Bu çalışmanın bulguları, otonom araç sürüş sistemlerinin tanıtılmasının başarısını artırmak için siber saldırıların oluşturduğu tehdidin ele alındığını ve öncelik verildiğini göstermiştir.

References

  • Abdel-Basset. M., Mohamed, M., Zhou, Y. & Hezam, I. (2017). Multi-criteria group decision-making based on the neutrosophic analytic hierarchy process. Journal of Intelligent and Fuzzy Systems, 333(6), 4055-4066.
  • Alkan N. & Kahraman C. (2023). Prioritization of Supply chain digital transformation strategies using multi-expert fermatean fuzzy analytic hierarchy process, Informatica, 34(1), 1-33.
  • Atanassov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96.
  • Boltürk E. & Kahraman C. (2018). A novel interval-valued neutrosophic AHP with cosine similarity measure. Soft Computing, 22(15), 4941-4958.
  • Buckley, J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247.
  • Büyüközkan, G. & Göçer, F. (2021). A novel approach integrating AHP and COPRAS under Pythagorean fuzzy sets for digital supply Chain partner selection. IEEE Transactions On Engineering Management, 68, 1486-1503.
  • Chang, D. (1986). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
  • Garg, H., Shahzadi, G. & Akram M. (2020). Decision-making analysis based on fermatean fuzzy yager aggregation operators with application in COVID-19 testing facility. Mathematical Problems in Engineering, 2020, Article ID 7279027, https://doi.org/10.1155/2020/7279027.
  • Garg, H., Ali, Z. & Mahmood, T. (2021). Algorithms for complex interval-valued q-rung orthopair fuzzy sets in decision-making based on aggregation operators, AHP, and TOPSIS. Expert Systems, 38(1), 1-36.
  • Gül, M. (2018). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and coloring unit. International Journal of Occupational Safety and Ergonomics, 26(4), 705-718.
  • Gündoğdu, F.K., Duleba, S., Moslem, S. & Aydın S. (2021). Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Applied Soft Computing, 100, 106920.
  • ISO 26262 - Parts [2-8] Requirements decomposition concerning ASIL tailoring (2011).
  • Jeevaraj, S. (2021). Ordering of interval-valued Fermatean fuzzy sets and its applications. Expert Systems with Applications, 185, 115613.
  • Kahraman, C., Oztaysi, B., Sari, I. & Turanoglu, E. (2016). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowl Based System, 59, 48-57.
  • Karasan, A., Ilbahar, E. & Kahraman, C. (2019). A novel Pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Computing, 23(21), 10953-10968.
  • Kirişci, M., Demir, I. & Simsek, N. (2022). Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection. Artificial Intelligence in Medicine, 127, 102278. https://doi.org/10.1016/j.artmed.2022.102278.
  • Kirişci, M. (2022a). Correlation Coefficients of Fermatean Fuzzy Sets with Their Application, J. Math. Sci. Model., 5(2), 16-23. https://doi.org/10.33187/jmsm.1039613.
  • Kirisci, M. (2022b). Data Analysis for Lung Cancer: Fermatean Hesitant Fuzzy Sets Approach, Applied Mathematics, Modeling and Computer Simulation, 30, 701-710. https://doi.org/10.3233/ATDE221087.
  • Kirişci, M. (2023). New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach. Knowl Inf Syst, 65, 855–868. https://doi.org/10.1007/s10115-022-01776-4.
  • Mary, F.R.P., Mohanaselvi S. & Broumi S. (2023). A solution approach to minimum spanning tree problem under Fermatean fuzzy environment. Bulletin of Electrical Engineering and Informatics, 12(3), 1738-1746.
  • Mathew, M., Chakrabortty, R. & Ryan, M. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988.
  • Öztaysi, B., Onar S., Boltürk E. & Kahraman C. (2015). Hesitant fuzzy analytic hierarchy process. 2015 IEEE International Conference Fuzzy Systems (FUZZ-IEEE), 2015, 1–7.
  • Saaty, T.L. (2008). The analytic hierarchy and analytic network measurement processes: Applications to decisions under Risk. European Journal of Pure and Applied Mathematics, 1(1), 122-196.
  • Sadiq, R. & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 23(1), 75–91.
  • Senapati, T. & Yager, R.R. (2019a). Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica, 30(2), 391-412.
  • Senapati, T. & Yager, R.R. (2019b). Fermatean fuzzy weighted averaging/geometric operators and their application in multi-criteria decision-making methods. Engineering Applications of Artificial Intelligence, 85, 112-121.
  • Senapati, T. & Yager, R.R. (2020). Fermatean Fuzzy Sets. J. Ambient Intell. Hum. Comp. 11, 663-674.
  • Son, T.D., Bhave, A. & der Auweraer, V. (2019). Simulation-based testing framework for autonomous driving development. IEEE International Conference on Mechatronics, 576-583. https://doi.org/10.1109/ICMECH.2019.8722847.
  • Van Laarhoven, P. & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst, 11(1–3), 229-241.
  • Wu, J., Huang, H. & Cao, Q. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multicriteria decision-making problems. Applied Mathematical Modelling, 37(24), 9898-9906.
  • Yager, R.R. (2013). Pythagorean fuzzy subsets. Proc. Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada.
  • Yager, R.R. (2014). Pythagorean membership grades in multi-criteria decision-making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965.
  • Zadeh, L.A. (1965). Fuzzy sets. Inf. Comp., 8, 338-353.
There are 33 citations in total.

Details

Primary Language English
Subjects Statistics
Journal Section Research Articles
Authors

Necip Şimşek 0000-0003-3061-5770

Murat Kirisci 0000-0003-4938-5207

Early Pub Date December 12, 2023
Publication Date December 31, 2023
Submission Date May 24, 2023
Published in Issue Year 2023 Volume: 22 Issue: 44

Cite

APA Şimşek, N., & Kirisci, M. (2023). A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH. İstanbul Commerce University Journal of Science, 22(44), 292-309. https://doi.org/10.55071/ticaretfbd.1300893
AMA Şimşek N, Kirisci M. A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH. İstanbul Commerce University Journal of Science. December 2023;22(44):292-309. doi:10.55071/ticaretfbd.1300893
Chicago Şimşek, Necip, and Murat Kirisci. “A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH”. İstanbul Commerce University Journal of Science 22, no. 44 (December 2023): 292-309. https://doi.org/10.55071/ticaretfbd.1300893.
EndNote Şimşek N, Kirisci M (December 1, 2023) A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH. İstanbul Commerce University Journal of Science 22 44 292–309.
IEEE N. Şimşek and M. Kirisci, “A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH”, İstanbul Commerce University Journal of Science, vol. 22, no. 44, pp. 292–309, 2023, doi: 10.55071/ticaretfbd.1300893.
ISNAD Şimşek, Necip - Kirisci, Murat. “A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH”. İstanbul Commerce University Journal of Science 22/44 (December 2023), 292-309. https://doi.org/10.55071/ticaretfbd.1300893.
JAMA Şimşek N, Kirisci M. A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH. İstanbul Commerce University Journal of Science. 2023;22:292–309.
MLA Şimşek, Necip and Murat Kirisci. “A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH”. İstanbul Commerce University Journal of Science, vol. 22, no. 44, 2023, pp. 292-09, doi:10.55071/ticaretfbd.1300893.
Vancouver Şimşek N, Kirisci M. A NEW RISK ASSESSMENT METHOD FOR AUTONOMOUS VEHICLE DRIVING SYSTEMS: FERMATEAN FUZZY AHP APPROACH. İstanbul Commerce University Journal of Science. 2023;22(44):292-309.