The rapid advancement of autonomous vehicle (AV) technologies is reshaping modern transportation, promising improved safety, efficiency, and sustainability. As these systems grow more complex, ensuring their reliability and adaptability in real-world conditions becomes a critical challenge. In this context, Digital Twins (DTs) have emerged as a transformative tool for modeling, simulating, and optimizing AV systems. A DT is a dynamic virtual replica of a physical system that mirrors its real-time state, behavior, and interactions with the environment. By integrating data from sensors, communication networks, and onboard computing units, DTs enable developers and researchers to simulate driving scenarios, assess vehicle performance, and predict potential failures before they occur in the real world. Digital Twin Approaches in Autonomous Vehicles explores the integration of digital twin technology with AV systems, highlighting both theoretical foundations and practical applications. This book provides comprehensive coverage of core concepts, methodologies, and applications of digital twins in vehicle design, testing, real-time simulation, predictive maintenance, safety validation, and connected vehicle ecosystems. Covering topics such as artificial intelligence, predictive maintenance, and vehicle-to-everything communication, this book is an excellent academic resource for graduate and doctoral students, engineers, technology developers, policymakers, and more.
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Shaik Mazhar Hussain is a distinguished academician and researcher with a robust background in Electrical Engineering. Currently serving as a faculty member in the Department of Computing and Electronics Engineering, he also holds the position of research coordinator at the Centre for Research and Consultancy (CRC). Dr. Hussain earned his PhD in Electrical Engineering in the year 2023 from the Department of Communications Engineering, specializing in Advanced Telecommunication Technology (ATT) at the school of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia. His doctoral research showcases his deep expertise and significant contributions to the field. Dr. Hussain educational journey includes a Postgraduate Certificate (PGCert) from Coventry University, United Kingdom, acquired in 2017, and a master's degree in Embedded Systems from Jawaharlal Nehru Technological University (JNTU), Hyderabad, in 2012. He commenced his academic pursuit with a bachelor's degree in Electronics and Communication Engineering from JNTU, Hyderabad, in 2008. Dr. Hussain has 15 years of teaching and research experience in his field. He has authored and published more than 30 papers in reputable journals and more than 20 papers in various conference proceedings. His research endeavors have been supported by numerous internal and external grants, underlining the significance and impact of his work. Dr. Shaik Mazhar Hussain further expanded his expertise by receiving the Professional Certificate of Competency in Fundamentals of Electric Vehicles from the Engineering Institute of Technology (EIT), West Perth, Australia. In addition to his academic and research achievements, Dr. Hussain is an active member of several professional bodies.
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Hardcover. Etat : new. Hardcover. The rapid advancement of autonomous vehicle (AV) technologies is reshaping modern transportation, promising improved safety, efficiency, and sustainability. As these systems grow more complex, ensuring their reliability and adaptability in real-world conditions becomes a critical challenge. In this context, Digital Twins (DTs) have emerged as a transformative tool for modeling, simulating, and optimizing AV systems. A DT is a dynamic virtual replica of a physical system that mirrors its real-time state, behavior, and interactions with the environment. By integrating data from sensors, communication networks, and onboard computing units, DTs enable developers and researchers to simulate driving scenarios, assess vehicle performance, and predict potential failures before they occur in the real world. Digital Twin Approaches in Autonomous Vehicles explores the integration of digital twin technology with AV systems, highlighting both theoretical foundations and practical applications. This book provides comprehensive coverage of core concepts, methodologies, and applications of digital twins in vehicle design, testing, real-time simulation, predictive maintenance, safety validation, and connected vehicle ecosystems. Covering topics such as artificial intelligence, predictive maintenance, and vehicle-to-everything communication, this book is an excellent academic resource for graduate and doctoral students, engineers, technology developers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798337377858
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Hardcover. Etat : new. Hardcover. The rapid advancement of autonomous vehicle (AV) technologies is reshaping modern transportation, promising improved safety, efficiency, and sustainability. As these systems grow more complex, ensuring their reliability and adaptability in real-world conditions becomes a critical challenge. In this context, Digital Twins (DTs) have emerged as a transformative tool for modeling, simulating, and optimizing AV systems. A DT is a dynamic virtual replica of a physical system that mirrors its real-time state, behavior, and interactions with the environment. By integrating data from sensors, communication networks, and onboard computing units, DTs enable developers and researchers to simulate driving scenarios, assess vehicle performance, and predict potential failures before they occur in the real world. Digital Twin Approaches in Autonomous Vehicles explores the integration of digital twin technology with AV systems, highlighting both theoretical foundations and practical applications. This book provides comprehensive coverage of core concepts, methodologies, and applications of digital twins in vehicle design, testing, real-time simulation, predictive maintenance, safety validation, and connected vehicle ecosystems. Covering topics such as artificial intelligence, predictive maintenance, and vehicle-to-everything communication, this book is an excellent academic resource for graduate and doctoral students, engineers, technology developers, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798337377858
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Hardcover. Etat : new. Hardcover. The rapid advancement of autonomous vehicle (AV) technologies is reshaping modern transportation, promising improved safety, efficiency, and sustainability. As these systems grow more complex, ensuring their reliability and adaptability in real-world conditions becomes a critical challenge. In this context, Digital Twins (DTs) have emerged as a transformative tool for modeling, simulating, and optimizing AV systems. A DT is a dynamic virtual replica of a physical system that mirrors its real-time state, behavior, and interactions with the environment. By integrating data from sensors, communication networks, and onboard computing units, DTs enable developers and researchers to simulate driving scenarios, assess vehicle performance, and predict potential failures before they occur in the real world. Digital Twin Approaches in Autonomous Vehicles explores the integration of digital twin technology with AV systems, highlighting both theoretical foundations and practical applications. This book provides comprehensive coverage of core concepts, methodologies, and applications of digital twins in vehicle design, testing, real-time simulation, predictive maintenance, safety validation, and connected vehicle ecosystems. Covering topics such as artificial intelligence, predictive maintenance, and vehicle-to-everything communication, this book is an excellent academic resource for graduate and doctoral students, engineers, technology developers, policymakers, and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798337377858
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