This thesis explores the innovative field of Digital Twin (DT) technology, with a particular focus on its applications in Autonomous Driving. A DT is a digital or virtual representation of a physical entity, product, system, process or asset. Data flows between the physical and digital objects are fully integrated in both directions. DTs use techniques such as modelling, shadowing, simulation, Machine Learning and interaction to support in designing, prototyping, monitoring, controlling, decision making and optimizing Cyber-Physical Systems. Engineering and implementing DTs is a time-consuming and complex process. Currently, DT design primarily focuses on a specific application domain, with an abstract perspective. The structure of DTs should integrate more flexibility, composability and extendability with regard to the development and implementation of DTs, regardless of the area of application. This thesis presents a conceptual meta-model for such a DT, based on the findings of research, comprehensive review, systematic categorization and abstraction of previous work in the field. Furthermore, a use case and its architectural framework are presented as well as a prototype DT implementation based on an existing physical entity, i.e. a Physical Twin (Arduino robot cars). A scenario-based evaluation of the architecture for the implementation of the prototype DT for Autonomous Driving was conducted. All the evaluated direct and indirect architecture-scenarios for DT extendability are very well supported. The thesis concludes with suggestions for avenues of future research that could be pursued.