Enhancing travel advisory zones for autonomous vehicles using interval-valued pythagorean fuzzy Yager-based decision-making

Autonomous Vehicles (AVs) represent a transformative technology with the potential to significantly reduce traffic incidents. This research addresses decision-making challenges for AVs by introducing novel Interval-Valued Pythagorean Fuzzy (IVPF) Yager Aggregation Operators (AOs). Yager AOs (YAOs) are highly versatile and effective at handling ambiguous decision-making (DM) problems. Through methodical analysis and the incorporation of attribute weights, these operators effectively solve multi-attribute decision-making (MADM) problems, leading to more robust decision-making in AV systems. This study introduces two novel Yager AOs: the interval-valued Pythagorean fuzzy Yager weighted averaging (IVPFYWA) operator and the interval-valued Pythagorean fuzzy Yager weighted geometric (IVPFYWG) operator. A novel score function for ranking interval-valued Pythagorean fuzzy numbers (IVPFNs) is formulated to address MADM challenges. Essential properties of the proposed operators, such as idempotency, monotonicity, and boundedness, are explored. Furthermore, an algorithm is devised to solve MADM problems using the proposed operators within the IVPF framework. These techniques are then applied to a practical MADM problem aimed at selecting the optimal travel advisory zone for AVs. A comparative analysis is conducted to demonstrate the validity and superiority of the proposed strategies over existing methods.