Introduction to road roughness

In addition to extracting information about road assets from videos using computer vision algorithms, RoadAI complements this data with road roughness data collected from connected vehicles. Combining these two types of road condition data creates a unique solution for road asset management.

Modern cars are mobile sensors capable of collecting various types of data, including the qualities of the road surface. The most common connected vehicle‑based road condition measurement relates to ride quality, which is typically measured with the International Roughness Index (IRI).

IRI is the roughness index most commonly obtained from measured longitudinal road profiles. Roughness index values starting from 0 (meters per km) indicate the level of irregularity in the longitudinal road profile, but the index also serves as an indicator for overall road condition. The higher index values indicate more irregularity and a poor road condition, whereas lower index values indicate less irregularity and a better road condition.

RoadAI provides road roughness data in two ways:

• RoadAI augments recorded videos with roughness measurements, making road roughness data available in RoadAI the same way as computer vision‑based road condition data. Only road roughness data from a specific time frame before the video recording date can be augmented to the video and is available for inspection and reporting. The road roughness data measurement closest to the recording date is selected amongst the available measurements and augmented to the video.
• As standalone, Road roughness feature layer provides access to frequently updating road roughness data, independent of collected videos. It also offers data overlays that display the change in road roughness values over time.