Road Condition Measurement and Assessment: A Crowd Based Sensing Approach

The widespread adoption of smart devices and vehicle sensors has
the potential for an unprecedented real time assessment of road conditions.
The international roughness index (IRI) is an important road profile quality
indicator well suited for a crowd based sensing approach. One of the
challenges, however, is the heterogeneous nature of sensor measurements from
multiple cars that need to be integrated. In this paper, we propose a
self-calibration approach that utilizes multiple statistical models trained
individually for each car, which in turn get integrated into an overall view
of the road segment’s IRI. We evaluate our approach on a dataset collected
from seven drives with a total distance of 32 km, with a smartphone equipped
car. The dataset contains GPS, accelerometer and gyroscope measurements. Our
results show that this approach can reach a mean R² of 0.68 for single car
predictions and a R² of 0.75 for combined predictions.