Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making

The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transporta...

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Bibliographic Details
Published inWorld electric vehicle journal Vol. 15; no. 9; p. 402
Main Authors Mahmood, Tahir, Idrees, Ahmad, Hayat, Khizar, Ashiq, Muhammad, Rehman, Ubaid ur
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Algorithms
Approximation
Artificial intelligence
artificial intelligence (AI) architecture
Automobiles
Autonomous cars
Autonomous vehicles
Collision avoidance
complex intuitionistic fuzzy rough set
Decision making
Energy conservation
Flexibility
Fuzzy logic
Fuzzy sets
Global positioning systems
GPS
Localization
Machine learning
MADM method
Operators (mathematics)
Planning
Sensors
Traffic accidents & safety
Transportation industry
Transportation systems
Uncertainty
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Summary:The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute decision-making (MADM) dilemma, as it requires making a complicated decision while considering a number of attributes, and these attributes can have two-dimensional uncertainty as well as indiscernibility. Thus, in this framework, we developed a novel mathematical framework “complex intuitionistic fuzzy rough set” for tackling both two-dimensional uncertainties and indiscernibility. We also developed the elementary operations of the deduced complex intuitionistic fuzzy rough set. Moreover, we developed complex intuitionistic fuzzy rough (weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric) aggregation operators. Afterward, we developed a method of MADM by employing the devised operators and investigated the case study “Selection of optimal AI architecture for autonomous vehicles” to reveal the practicability of the devised method of MADM. Finally, to reveal the dominance and supremacy of our proposed work, a benchmark dilemma was used for comparison with various prevailing techniques.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj15090402