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Title	Intelligent Multiple Vehicule Detection And Tracking Using Deep-Learning And Machine Learning: An Overview
ID_Doc	32453
Authors	Ben Youssef M.; Salhi A.; Ben Salem F.
Year	2021
Published	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
DOI	http://dx.doi.org/10.1109/SSD52085.2021.9429331
Abstract	Autonomous vehicles are in full development and vehicles classification is a fundamental part of this new technology in this years. The AVs is very is important solution in smart cities. The electric vehicle EV is one of the solutions recommended by the vehicle manufacturers and research organizations to reduce noise pollution, fuel consumption, real time and time execution for tasks in networking EV. The transport and mobility sector has for more than a decade seen a fundamental change in its organization due to a double technological revolution and use a new forms of mobility. This motion has profound consequences on the relationship that citizens maintain with mobility. It also proposes a physical platform able to perform a form of platooning using Artificial Intelligence (AI) with scheduler to create platoons with miniature vehicle. Platform called Autonomous Learning Intelligent Vehicles is the association of multiple physical cars coupled with an infrastructure, which handles the data of every single car and makes decisions based on the augmented environmental. In this solution, we present tracking and detection of EVs in parking or in station. So, we change communications EVs, giving priority to charging demands over other types of sms lower priority messages. Finally, we propose an efficient admission control mechanism to manage EVs traffic and to provide quality of Service to charging demand messages in terms of strict delay to avoid both a long latency of EV users and a network overload in high offered load conditions. © 2021 IEEE.
Author Keywords	Electronic Vehicle 'VE'; Embedded System; IA; IoT; Localization; Machine learning; multiple streams processing; physical vehicle; QoS; Scheduling tasks; Self driving; Wireless communication

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