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Article type: Research Article
Authors: Boubakri, Anis* | Mettali Gammar, Sonia
Affiliations: National School of Computer Sciences, University of Manouba, Manouba, Tunisia
Correspondence: [*] Corresponding author: Anis Boubakri, National School of Computer Sciences, University of Manouba, Tunisia. E-mail: anis.boubakri@esprit.tn.
Abstract: Autonomous vehicle platoons have become an increasingly popular solution to enhance driving system performance and facilitate the safe integration of these vehicles on the roads. To ensure efficient platoon traffic, it is essential to effectively manage both speed and direction within this group of vehicles. It is in this context that this research is situated. Our primary objective is to enhance the performance of speed and direction controllers, as the ability of vehicles to adjust these parameters in a coordinated manner is crucial for the success of platoon traffic. To achieve this, we have developed a novel speed control approach based on neural networks and fuzzy logic, utilizing V2X communication. By incorporating environmental parameters through vehicle-to-vehicle communication and considering the specific goals of the platoon, our neuro-fuzzy system can accurately calculate optimal speeds and directions for the vehicles. Our experiments have demonstrated the effectiveness of this approach compared to traditional and advanced methods, improving both energy efficiency and temporal coordination of autonomous vehicles within the platoon.
Keywords: V2V connectivity, platoon, connected autonomous vehicles, supervised neuro-fuzzy controller, driving system
DOI: 10.3233/KES-230036
Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 28, no. 3, pp. 517-537, 2024
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