Automatic Lane-level Road Network Graph-generation from Floating Car Data

While common digital road network graphs are able to represent real-world street network topology relations quite adequately, they are highly generalized with regard to the composition of a road. Irrespective of their actual number of lanes, roads are shown as just one single line. As many intelligent transportation systems (ITS) applications require or provide lane-specific data and services, this is no longer sufficient from a shortto mediumterm perspective. In particular, automated driving requires high-accuracy graphs both in topology and in geometry to localize positions not only on the correct road, but also in the correct lane. In the following paper, a cost-effective methodology for deriving such lanelevel road network graphs will be described. The methodology is applied to standard GNSS trajectories collected for three different road types (urban, interurban, motorway) by vehicles participating in real-world traffic situations (Floating Car Data). The methodology extracts the number and position of lane centrelines from pre-processed GNSS trajectories using a kernel density estimation (KDE) and distance relations. Results show that the proposed method can, depending on the quality of the input data, reliably model lane centrelines for different road settings.