Longitudinal / Transverse Evenness

Data on the condition of road surfaces form the basis for planning maintenance work on the road network. The standard data measured during condition assessment and evaluation includes skid resistance, surface distress (cracks, patches, potholes, etc.) and the longitudinal and transverse evenness of the road. For example, the theoretical water depth in ruts causing aquaplaning and consequently a large number of accidents can be calculated from the longitudinal and transverse evenness together with the transverse inclination of the road. Longitudinal and transverse evenness are derived from the detailed three-dimensional height profile of the road surface.

Pavement Profile Scanner PPS

The PPS from Fraunhofer IPM generates a detailed three-dimensional height profile of the road surface. The shoebox-sized scanner is mounted on a measuring vehicle above the road surface. With a single eye-safe laser beam, the PPS scans the road over a width of approximately 4 meters. The distance to the road is determined by the transit time between emitted and reflected light. The laser scans the surface 800 times per second perpendicular to the forward movement of the vehicle. Each of the profiles generated in this way consists of approximately 900 to 1,800 measuring points, depending on the selected measuring frequency. Even road unevenness in the range of less than 0.2 mm can still be registered.

The measuring speed allows the vehicle to operate in regular traffic – inner-city roads as well as motorways can be monitored in this way without impairing traffic.

Additional recording of clearance gauge

With the Clearance Profile Scanner CPS, Fraunhofer IPM also offers a completely encapsulated 360° scanner that captures the environment with millimeter accuracy. All of our scanners can be integrated into the Mobile Urban Mapper MUM concept together with cameras and precise positioning technology from Fraunhofer IPM, thus providing all data of the street and its surroundings.

Fully automated data evaluation

Through the integration of 3DAI – our Deep Learning Framework with automated interpretation of 2D and 3D data – we enable an automated, efficient evaluation of the extensive data sets. The result can, for example, be a map that only shows objects such as street assets or cracks as required.