Digital twin technologies for automated vehicles in smart healthcare systems
subject: autonomous vehicles; electronic automation; Health 4.0; Industry 4.0; Internet of Things; manufacturing systems
description: The idea of being comfortable seems appealing to a vast majority of people, from the start humans were always dependent on something. First the tools were invented and with the help of the tools, amazing things were built. From the invention of the wheel to the steam-powered machines and now the introduction of electronic automation, digitization and making intelligent production processes is the need for todays industry. Industry 4.0 is now the standard by which businesses must measure their progress. It enables businesses to reinvent themselves. Manufacturing systems go beyond simple connections here, communicating, analyzing, and using data to drive more intelligent activities. It combines Internet of Things (IoT), analytics, additive manufacturing, robots, artificial intelligence, sophisticated materials, and augmented reality. The autonomous vehicle (AV) is one of the applications of Industry 4.0. AVs can make passenger transfers more efficient. Furthermore, smart sensors, when combined with cognitive computing and IoT, portray an AV as a cyber-physical system where data from all relevant viewpoints is closely monitored and synced between physical devices and the cyber computational realm. By utilizing sophisticated information analytics, AVs will be able to work more effectively, collaboratively, and resiliently. As a result, AVs might be able to work with Industry 4.0 systems. 2023 Elsevier Inc. All rights reserved.
creator: Indrakumari R.; Poongodi T.; Sagar S.; Vijayalakshmi
source: Digital Twin for Smart Manufacturing, pp. 161-184.
publisher: Elsevier
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-032399205-3; 978-032395840-0
format: Online
language: English
type: Book chapter
coverage: Indrakumari R., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Poongodi T., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Sagar S., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Vijayalakshmi, Department of Data Science CHRIST (deemed to be University), Maharashtra, Lavasa, India