A scalable, self-supervised calibration and confounder removal model for opportunistic monitoring of road degradation

Assessing road degradation typically requires specialized hardware (such as laser profilometers) or labor-intensive visual inspection. To facilitate large-scale, timely inspection of road surfaces, opportunistic sensing is proposed: Sound and vibration measurements are obtained from vehicles that are on the road for other purposes than measuring road quality. Prior work has addressed the problem of calibration and measurement noise removal from this abundance of measurements for a small number of measurement vehicles that drive on the same roads. However, as the deployment of opportunistic monitoring progresses, the applied techniques suffer from scalability. Here, a scalable self-supervised calibration and confounder removal (SCCR) algorithm is introduced. It allows to self-calibrate even if the data collection is done in distinct geographic areas and is capable of generalizing to vehicles not encountered during the training phase. Several model design alternatives are explored. After the application of SCCR, supervised training on a small subset of roads allows to predict observations made by standardized techniques also in areas where the latter have not been performed. The approach is tested and validated with 41 cars driving on 23,000 km of roads