Live Road Assessment Custom Dataset (LiRA-CD), an open-source dataset for road condition modelling and research. The aim of this dataset is to provide researchers and pavement engineers with a vehicle dataset that is open-source and suitable for developing large-scale road condition monitoring methods. Specifically, this dataset focuses on linking in-vehicle sensor data collected by regular cars to standard road condition parameters utilized by public road agencies (i.e., parameters used as input for planning and management of road networks).
The LiRA-CD contains 1796 km of road data from highway and urban roads in the Copenhagen area collected during the LiRA project.
LiRA-CD is freely available and can be accessed at https://doi.org/10. 11583/DTU.c.6659909 .
Furthermore the following datasets is available:
Data subset for road condition modelling – car sensor data : https://doi.org/10.11583/DTU.23192909.v1
Data subset for road condition modelling – reference data:
https://doi.org/10.11583/DTU.23097002.v1
Data subset for road condition modelling . platoon friction test:
https://doi.org/10.11583/DTU.23096600.v1
LiRA Vis web application
LiRA Vis Web Application. Source code of the LiRA Vis web application, which shows and visualizes the data collected from car sensors and the derived road conditions from the car data on a map.
https://doi.org/10.11583/DTU.22178375
Road profile Inversion based on in-Vehicle Accelerations (RIVA)
MATLAB package for road profile inversion from in-vehicle accelerometer readings : https://doi.org/10.11583/DTU.24659268.v1
Relevant publications
Skar, Asmus; Alstrøm, Tommy Sonne; Brüsch, Thea; Vestergaard, Anders Malherbes; Larsen, Jakob; M. Pour, Shahrzad; et al. (2023). Live Road Assessment Custom Dataset (LiRA-CD). Technical University of Denmark. Collection. https://doi.org/10.11583/DTU.c.6659909
Asmus Skar, Anders M. Vestergaard, Thea Brüsch, Shahrzad Pour, Ekkart Kindler, Tommy Sonne Alstrøm, Uwe Schlotz, Jakob Elsborg Larsen, Matteo Pettinari (2023). LiRA-CD: An open-source dataset for road condition modelling and research. Data in Brief, Volume 49. https://doi.org/10.1016/j.dib.2023.10942