Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Road condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz.