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Title	Deep Learning-Based Autonomous Driving And Cloud Traffic Management System For Smart City
ID_Doc	17938
Authors	Syamal S.; Datta J.; Basu S.; Das S.
Year	2023
Published	Convergence of IoT, Blockchain, and Computational Intelligence in Smart Cities
DOI	http://dx.doi.org/10.1201/9781003353034-7
Abstract	The implementation of cloud traffic control systems and autonomous driving is the future scenario of smart cities. This chapter proposes a Sense, Learn, and Act (SLA) model, with an algorithm to drive a car autonomously through the streets of a smart city, where the traffic is controlled and managed by an advanced cloud system containing a secure database. The design of this system synchronizes transportation and traffic control systems simultaneously in real time. The research proposal introduces a present-time, cutting-edge transportation system with increased security and almost zero probability of traffic mishaps. It includes a real-time system with intercommunicating traffic signals that not only governs vehicle speed but also manages the different aspects of environmental perception, planning, and control. The work depicted in this chapter is trying to modernize the contemporary transportation system and secure vehicle-to-traffic data transfer. Here, every type of vehicle is an individual node of the unified cloud system with a priority identity (ID). By applying image detection of a traffic signal with computer vision, the specific traffic node additionally provides the real-time signal state to control the speed of a vehicle and establish a two-way communication to make the whole system work in harmony throughout the smart city. © 2024 selection and editorial matter, Rajendra Kumar, Vishal Jain, Leong Wai Yie and Sunantha Teyarachakul; individual chapters, the contributors.
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