Each year, the development of a fully functioning autonomous vehicle is closer, but there are still many areas for improvement. One of these is a simulation, which is needed to develop and test automated driving systems, and for simulation, detailed modeling of road spaces is necessary. Therefore, the general task of this Master’s thesis was to prepare different available geodata for simulation applications in the connected and automated mobility domain. Different spatial datasets were provided by the mapping agency Ordnance Survey, which also co-supervised this project to enable the work to be completed. More precisely, two different road network data were provided that are real-world based and defined in standardized formats. This project examined the feasibility of developing a methodology for converting and improving existing data from the Ordnance Survey to create OpenDRIVE-compliant road networks for driving simulations. The methodology is based on gap analysis to determine what is needed to improve the existing data and on different geoinformatics and geometric approaches to enable data conversion. In the master thesis, different tools were used, such as FME and the programming language Kotlin to perform the gap analysis and data conversion, and the OpenDRIVE viewer at the end for visualizing the results.     «

Each year, the development of a fully functioning autonomous vehicle is closer, but there are still many areas for improvement. One of these is a simulation, which is needed to develop and test automated driving systems, and for simulation, detailed modeling of road spaces is necessary. Therefore, the general task of this Master’s thesis was to prepare different available geodata for simulation applications in the connected and automated mobility domain. Different spatial datasets were provided...     »