Continuous Intuitionistic Fuzzy AHP & CODAS Methodology for Automation Degree Selection

The automotive industry's evolution thrives on technological innovation, prioritizing efficiency, safety, and sustainability. Recent improvements in autonomous driving and IoT integration have revolutionized vehicle design, safety, and maintenance with different automation degrees from partial human control to full automation. Selecting these automation degrees involves complicated Multi-Criteria Decision-Making (MCDM) encompassing technical feasibility, societal impact, and regulatory compliance. Utilizing Analytic Hierarchy Process (AHP) and Combinative Distance-Based Assessment (CODAS) offers a structured framework to navigate these complexities. AHP establishes criteria importance, while CODAS handles uncertainties, enabling informed decisions balancing technology with ethical, societal, and regulatory considerations. Fuzzy extensions further refine these methodologies, empowering the industry to adeptly address subjective perceptions and ambiguous data, enhancing the decision-making framework for automotive technology evolution. This paper navigates the intricate landscape of automation degree selection within the automotive industry evolution, employing a structured approach merging fuzzy AHP and fuzzy CODAS methods by utilizing Continuous Intuitionistic Fuzzy Set (CINFUS). This approach not only brings a new perspective to autonomous vehicles but also highlights the importance of choosing the right automation degree. Moreover, a sensitivity analysis involved adjusting the weights assigned to different criteria within the Continuous Intuitionistic Fuzzy (CINFU) AHP framework. By systematically altering these weights and observing their impact on the final automation degree selection, decision-makers can understand the sensitivity of the chosen automation degree to changes in priority among criteria.