DAS benchmarking methodology based on FWZIC II and FDOSM II to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 9; pp. 12747 - 12774
• Main Authors: Mahmoud, U. S., Albahri, A. S., AlSattar, H. A., Zaidan, A. A., Talal, M., Mohammed, R. T., Albahri, O. S., Zaidan, B. B., Alamoodi, A. H., Qahtan, Sarah
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer Nature B.V
• Subjects: Advanced driver assistance systems; Artificial Intelligence; Automobile industry; Benchmarks; Computational Intelligence; Criteria; Data acquisition; Datasets; Efficiency; Engineering; Fuzzy sets; Methodology; Original Research; Parameter estimation; Ranking; Robotics and Automation; Sensitivity analysis; Sensors; Signal processing; Software; User Interfaces and Human Computer Interaction; Vehicles; FWZIC; FDOSM; Intuitionistic fuzzy sets; Data acquisition system; Intelligent transportation system; Multi-criteria decision making
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-022-04201-4

This study proposed a novel methodology of data acquisition systems (DASs) benchmarking based on fuzzy-weighted zero-inconsistency (FWZIC II) and fuzzy decision by opinion score method (FDOSM II), which are applied in an intuitionistic fuzzy set (IFS) context and account for hesitation when benchmarking DASs, to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves constructing a decision matrix based on the intersection of the DAS alternatives and criteria. The second phase (development phase) proposes the formulation of a novel FWZIC II to weight the criteria and the formulation of a novel FDOSM II to benchmark DASs. Fourteen DASs were benchmarked based on the 15 DAS criteria, which included seven sub-criteria for ‘comprehensive complexity assessment’ and eight sub-criteria for ‘design and implementation’, which had a significant effect on the DAS design when implemented by industrial communities. A systematic ranking and sensitivity analysis were conducted to demonstrate that the benchmarking results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criteria weight values.