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Open Access

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad 500090, India
Institute of Computer Science and Digital Innovation (ICSDI), UCSI University, Kuala Lumpur 56000, Malaysia, and also with Department of ECE, Koneru Lakshmaiah Education Foundation, Hyderabad 500090, India
ICSDI, UCSI University, Kuala Lumpur 56000, Malaysia
Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Kingdom of Saudi Arabia
Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Kingdom of Saudi Arabia
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In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications. However, the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy efficient. From existing methods, it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy efficiency. However, the models focus on different aspects separately. There is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence (AI) on autonomous driving and energy efficiency. Towards this end, we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision making. It focuses on both safe driving in highway scenarios and energy efficiency. The deep learning based framework is realized with many models used for localization, path planning at high level, path planning at low level, reinforcement learning, transfer learning, power control, and speed control. With reinforcement learning, state-action-feedback play important role in decision making. Our simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.



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Tsinghua Science and Technology
Pages 1776-1784
Cite this article:
Ramesh G, Budati AK, Islam S, et al. Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models. Tsinghua Science and Technology, 2024, 29(6): 1776-1784.








Web of Science






Received: 10 July 2023
Revised: 03 August 2023
Accepted: 28 August 2023
Published: 20 June 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (