(National Research Development and Innovation Fund (TKP2020 National Challenges Subprogram,
Grant No. BME-NC))
Digital twins of road surfaces support multiple engineering applications. Remote sensing
technologies provide information from the entire surface of the pavement by high accuracy
point clouds. Pavement errors and differences from designed geometry can be detected
and assessed using such datasets, while OpenCRG models derived from point clouds support
transportation applications. High-resolution CRG (Curved Regular Grid) models enable
analyzing vehicle suspension systems in vehicle dynamics simulation environments.
Furthermore, such models also support creating the digital twins of vehicle suspensions
and improve the development and research of models related to vehicle dynamics. The
paper presents how the suspension digital twin was obtained applying a genetic algorithm
and how it was assessed. The quality of raw data and that of the derived methods are
analyzed in the case of multiple mapping technologies (terrestrial, mobile, and aerial
laser scanning). CRG models were created from all datasets, and their applicability
was investigated to support vehicle simulations with high accuracy demand. Other important
vehicle-related use cases are also mentioned in the paper.
