Generating a Lane-Specific Transportation Network Based on Floating-Car Data
Future applications in ITS and automated driving require high precise digital maps including a lane-specific transportation network. The paper presents a method for estimating lane center lines based on vehicle trajectories from floating-car data. Kernel
- PDF / 309,321 Bytes
- 13 Pages / 439.37 x 666.142 pts Page_size
- 94 Downloads / 192 Views
tract Future applications in ITS and automated driving require high precise digital maps including a lane-specific transportation network. The paper presents a method for estimating lane center lines based on vehicle trajectories from floating-car data. Kernel density estimation was applied for estimating lane center lines. The floating-car dataset is based on measurements on three different road types (urban 3-lane freeway, urban arterial, rural 2-lane freeway) using different low-cost GNSS receivers (GPS data logger and several smartphone GPS positioning apps). As reference, some test runs were conducted with high precise D-GPS measurement equipment. The longitudinal and lateral positioning errors were analyzed within a roadway and trip based distance analysis. The final results show deviations less than 0.14 m in median between measured and estimated lane center lines. This accurate estimation of lane center lines allows a generation of lane-specific transportation networks based on common floating-car data. R. Neuhold (&) M. Haberl M. Fellendorf Institute of Highway Engineering and Transport Planning, Graz University of Technology, Rechbauerstr. 12, 8010 Graz, Austria e-mail: [email protected] M. Haberl e-mail: [email protected] M. Fellendorf e-mail: [email protected] G. Pucher M. Dolancic TraffiCon—Traffic Consultants GmbH, Strubergasse 26, 5020 Salzburg, Austria e-mail: Pucher@trafficon.eu M. Dolancic e-mail: Dolancic@trafficon.eu M. Rudigier Virtual Vehicle Research Center, Inffeldgasse 21a, 8010 Graz, Austria e-mail: [email protected] J. Pfister pwp-systems GmbH, Prießnitzstr. 11, 65520 Bad Camberg, Germany e-mail: Pfi[email protected] © Springer International Publishing Switzerland 2017 N.A. Stanton et al. (eds.), Advances in Human Aspects of Transportation, Advances in Intelligent Systems and Computing 484, DOI 10.1007/978-3-319-41682-3_84
1025
1026
R. Neuhold et al.
Keywords Lane-specific transportation network Floating-car data surement devices Distance analysis Kernel density estimation
GPS mea-
1 Introduction Transport planners usually model roads as one single edge between two nodes (e.g. intersections) in transportation networks, irrespective of the number of lanes. Therefore, single lines as part of an entire road graph represent the road sections. Often lane-specific information like the number of lanes is included in additional attributes of the graph. This generalization of road geometries reduces the resolution of the data as well as costs in the development and maintenance of a transportation network [1]. However, existing and emerging ITS services might require digital road network graphs with a higher level of detail and accuracy regarding the representation of lane center lines. Cooperative services, for instance, often either need the lane specific localization of messages or provide information for specific lanes [2]. Examples would be lane departure warnings, local hazard messages (e.g. road bumps, accidents, congestion) or lane specific route info
Data Loading...
