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.

中文翻译：

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用于道路退化机会监测的可扩展、自监督校准和混杂消除模型

评估道路退化通常需要专门的硬件（例如激光轮廓仪）或劳动密集型的目视检查。为了便于大规模、及时地检查路面，提出了机会传感：声音和振动测量是从道路上的车辆获得的，用于测量道路质量以外的其他目的。先前的工作已经解决了在相同道路上行驶的少量测量车辆的大量测量结果中校准和测量噪声消除的问题。然而，随着机会监控部署的进展，所应用的技术受到可扩展性的影响。在这里，介绍了一种可扩展的自监督校准和混杂消除 (SCCR) 算法。即使数据收集是在不同的地理区域完成的，它也允许进行自我校准，并且能够推广到训练阶段未遇到的车辆。探索了几种模型设计替代方案。在应用 SCCR 之后，在一小部分道路上进行监督训练，可以预测标准化技术在尚未执行标准化技术的区域进行的观察。该方法在 23,000 公里道路上行驶的 41 辆汽车上进行了测试和验证。