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Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Manar Ahmed Hamza1,*, Masoud Alajmi2, Jaber S. Alzahrani3, Siwar Ben Haj Hassine4, Abdelwahed Motwakel1, Ishfaq Yaseen1
1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
4 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(2), 3465-3479. https://doi.org/10.32604/cmc.2022.026066

Received 14 December 2021; Accepted 21 February 2022; Issue published 29 March 2022

Abstract

Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS) energy management scheme to accomplish the desired engine torque based on distinct parameters. In addition, the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer (BMO). For accurate TFP, the bidirectional gated recurrent neural network (Bi-GRNN) model is used in AVs. A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics. The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

Keywords

Sustainable energy; transportation; energy management; traffic flow prediction; soft computing; deep learning

Cite This Article

M. Ahmed Hamza, M. Alajmi, J. S. Alzahrani, S. Ben Haj Hassine, A. Motwakel et al., "Sustainable energy management with traffic prediction strategy for autonomous vehicle systems," Computers, Materials & Continua, vol. 72, no.2, pp. 3465–3479, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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