An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration

Abstract

The rapid advancement of Connected Autonomous Vehicles (CAVs) equipped with self-powered sensors is poised to revolutionize road safety, efficiency, and the quality of travel. However, the effective integration of these technologies within dynamic road environments poses significant challenges, highlighting the need for innovative multi-criteria decision-making (MCDM) approaches to optimize their deployment. This study tries to solve the integration problem by proposing an MCDM method that uses fuzzy sets to evaluate and rank different scenarios for better performance of augmented intelligence and the Internet of Things (IoT) in CAVs. This research uses two key techniques: the Fuzzy Logarithm of Incremental Weights (F-LMAW) method for criteria evaluation and the Fermatean Fuzzy Weighted Aggregated Sum Product Assessment (FF-WASPAS) method for scenario evaluation. To ensure the reliability and accuracy of the results, a sensitivity analysis was conducted. The analysis confirmed the effectiveness of the proposed approach. The study's results showed that the third scenario (the integration of augmented intelligence and IoT in urban areas via self-powered sensors) got the highest score, which shows how important it is compared to the other choices. The obtained results highlight the importance of integrating augmented intelligence and IoT technologies to enhance public transportation with autonomous vehicles equipped with self-powered sensors.

Keywords

• CAVs
• Self-powered sensors
• Augmented intelligence
• IoT
• F-LMAW
• FF-WASPAS