A Novel Extended EDAS in Minkowski Space (EDAS-M) Method for Evaluating Autonomous Vehicles

摘要

Multi-Criteria Decision-Making (MCDM) methods have a significant influence on decision making in a variety of strategic fields, including science, business, and real-life studies. These methods also effectively support researchers in solving the emerging issues that may be encountered during their research activity. This work introduces a new Evaluation method based on the Distance from the Average Solution in the Minkowski space (EDAS-M). The main contribution of this study is the EDAS-M based MCDM model for the ^valuation of an autonomous vehicle. Besides, the CRITIC (Criteria Importance Through Intercriteria Correlation) was used to determine objective criteria weights. The EDAS-M method provides a modified extension of the conventional Evaluation method based on the Distance from the Average Solution (EDAS) method. Seven different MADM methods are used to compare problem-solving results. Namely, the EDAS, WASPAS (Weighted Aggregated Sum Product Assessment), SAW (Simple Additive Weighting), ARAS (Additive Ratio Assessment), TOPSIS (Technique for Order Preference by Similarity Ideal Solution), TOPSIS-M (TOPSIS Minkowski space) and MABAC (Multi-Attributive Border Approximation Area Comparison) techniques validate the stability of the results obtained by using the new method above mentioned. Sensitivity analysis reflects the dynamics of the influence of dynamic matrices. It showed a high correlation of positions with all applied approaches. This correlation has also been maintained in a dynamic environment.