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Driving requires continuous decision making from a driver taking into account all available relevant information. Automating driving tasks also automates the related decisions. However, humans are very good at dealing with bad quality, fuzzy, informal and incomplete information, whereas machines generally require solid quality information in a formalized format. Therefore, the development of automated driving functions relies on the availability of machine-usable information. A digital twin contains quality controlled information collected and augmented from different sources, ready to be supplied to such an automated driving function. An information model that describes all conceivably relevant information is necessary. To this end, a list of requirements that such an information model should meet is proposed and each requirement is argued for. Based on the anticipated services and applications that such a system should support, a collection of requirements for system architecture is derived. Information modeling is performed for selected relevant information groups. A system architecture has been proposed and validated with three different implementations, addressing several different applications to support decisions at a highway tunnel construction site in Austria and throughout the Test Bed Lower Saxony in Germany.

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Automated driving, in general, and platooning, in particular, represent a highly active field of research. The idea to automate traffic is closely related to high expectations in both individual and public transport. However, the complexity of automated driving requires methods beyond the traditional development approaches. This chapter describes a state-of-the-art methodology to organise and systematically address a comprehensive set of research questions in the context of truck platooning. Following best practices, an evaluation design is presented, which ensures the alignment of research efforts with the actual research agenda, that is, to answer the right questions. Specifically, the benefits of automated driving and their conflicting relationships are explored and the entities that affect automated driving performance and their interactions are presented. Finally, a solution concept that adequately addresses the complexity and the stochastic nature of the problem is presented. The solution concept consists of several key methods such as scenario-based design and stochastic simulation, data mining and complexity and robustness management.