Infrastructure-based digital twins for cooperative, connected, automated driving and smart road services

Elvira Thonhofer, Simon Sigl, Martin Fischer, Fin Heuer, Andreas Kuhn, Jacqueline Erhart, Manfred Harrer, Wolfgang Schildorfer

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)311-324
Number of pages14
JournalIEEE Open Journal of Intelligent Transportation Systems
Publication statusPublished - 13 Apr 2023


  • Automated driving
  • cooperative connected automated mobility
  • decision support system
  • digital twin
  • information modelling
  • test bed


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