A novel algorithm for autonomous parking vehicles using adjustable probabilistic neutrosophic hesitant fuzzy set features

This article presents a novel algorithm for autonomous parking vehicles (AVs). The idea is driven by the fact that AVs are the face of the future, and for their sustenance, the environment must be suitable for enduring technological advancements. This manuscript ensures the proper utilization of the parking space of AVs, and the proposed algorithm ensures the consideration of appropriate criterion information to be withheld from the focus. For it, the presented manuscript introduces the notion of the adjustable probabilistic neutrosophic hesitant fuzzy set (Ad-PNHFS). This set can tackle the hesitant information considering corresponding probability values but with adjustable capacities. These capacities offer the decision-maker the liberty to choose over two sets of operational laws based on the specific tolerance level. Ad-PNHFSs eliminate the existing dubiety on treating the probabilities and avoid them from attaining the zero value in the state of multiple hesitant values. In developing the advanced functionalities of the proposed set, this manuscript focuses on presenting the adjustable probabilistic neutrosophic hesitant Einstein weighted average (Ad-PNHFEWA) as well as geometric (Ad-PNHFEWG) aggregation operators (AOs). An efficient decision-making (DM) approach has been proposed by introducing a weight determination model. The process is applied to the parking of AVs, and its comparisons are made with the existing theories.

Automated summary

This article presents a new algorithm for autonomous parking vehicles (AVs). It focuses on the proper utilization of AVs' parking space and introduces the concept of adjustable probabilistic neutrosophic hesitant fuzzy set (Ad-PNHFS) to handle hesitant information with corresponding probability values and adjustable capacities. The article also introduces adjustable probabilistic neutrosophic hesitant Einstein weighted average (Ad-PNHFEWA) and geometric (Ad-PNHFEWG) aggregation operators. An efficient decision-making approach and comparison with existing theories are provided.