Digital Twin Approaches in Autonomous Vehicles

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.