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

作　　者：Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai 

机构地区：[1]Department of Computer Science and Engineering,Gokaraju Rangaraju Institute of Engineering&Technology,Hyderabad 500090,India [2]Institute of Computer Science and Digital Innovation(ICSDI),UCSI University,Kuala Lumpur 56000,Malaysia [3]also with Department of ECE,Koneru,Lakshmaiah Education Foundation,Hyderabad 500090,India [4]ICSDI,UCSI University,Kuala Lumpur 56000,Malaysia [5]Department of Software Engineering,College of Engineering,University of Business and Technology,Jeddah 21448,Kingdom of Saudi Arabia [6]Department of Computer Science,Applied College,University of Tabuk,Tabuk 47512,Kingdom of Saudi Arabia

出　　处：《Tsinghua Science and Technology》2024年第6期1776-1784,共9页清华大学学报自然科学版（英文版）

基　　金：the Ministry of Higher Education Malaysia for funding this research project through Fundamental Research Grant Scheme(FRGS)(No.FRGS/1/2022/TK02/UCSI/02/1)and also to UCSI University,Malaysia.

摘　　要：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.

关 键 词：wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al) 

分 类 号：U469.72[机械工程—车辆工程] TP18[交通运输工程—载运工具运用工程]