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The growth of the 4th industrial revolution (Industry 4.0) and the improved implementation of Big Data are motivating the need for datadirected and decision-making in manufacturing. Digital Twin (DT) is one of the top data-directed and decision-making concepts allowing businesses and manufacturers to simulate products to build faster, cost-effective, and high-quality products. The motorized (Automotive / Vehicle) Industry is estimated to hold 15% of digital twin use cases last year. DT technology has contributed from the starting stage of design to the final construction stages of vehicles and also helps the driver of the vehicle to draw useful information from its daily functions and makes the driver more comfortable and safer. [ABSTRACT FROM AUTHOR]

Digital twin technology has gained significant attention in recent years as a promising approach for improving the performance and efficiency of various systems including vehicular networks. Vehicular networks are critical for intelligent transportation systems, providing communication and coordination among vehicles, roadside infrastructure, and other entities to enable efficient traffic management, enhanced safety, and improved driving experiences. SDN is a potential networking architecture that isolates the control plane from the data plane, enabling centralized administration and control of network resources. SDN provides programmability, flexibility, and scalability, making it well-suited for managing the complex and dynamic nature of vehicular networks. Combining digital twin technology with SDN can enable intelligent management and control of vehicular networks, leading to improved performance, enhanced reliability, and efficient resource utilization. In this paper, a novel framework is proposed that leverages digital twin technology in vehicular networks using SDN. The architecture presented integrates digital twin models with SDN controllers, enabling real-time monitoring, analysis, and control of vehicular network components. The digital twin models are used to represent virtual replicas of physical vehicular components, such as vehicles, roadside units, and traffic signals, providing a holistic view of the vehicular network's behavior and performance. The digital twin models are discussed and can be used for various vehicular network management tasks, including traffic flow optimization, congestion detection and mitigation, predictive maintenance, and incident management. [ABSTRACT FROM AUTHOR]