Rapid measurements of large-scale pavement roughness have long been a hot topic in pavement condition evaluation and maintenance. Most traditional methods rely on dedicated devices, such as laser, Lidar and so on, which should be set up on customized vehicles. With the rapid development of sensing technology, vehicles owned by the general public are empowered with the ability to collect vibration measurements themselves. This crowdsourced dataset is convenient, extensive coverage, inexpensive, and has high sampling frequency, making it a suitable source for large-scale pavement roughness evaluation. However, vehicle information is missing for these data due to privacy protection, which renders them quite difficult to directly use with traditional model-based methods. Thus, in this paper, we propose a semi-supervised learning (SSL) model to deal with the problem of incomplete data and multi-vehicle data fusion. A mathematical derivation of the ‘international roughness index’ (IRI) using in-car vibrations is established. Furthermore, given the multi-vehicle scenario, a self-training model is designed to iteratively estimate IRIs in a roadway network. Both the confidences of the vehicle parameters and IRI estimation are considered in the algorithm to improve its reliability and robustness. A full-car simulation model is constructed to verify the effectiveness of the proposed model. The results show that the overall relative error is less than 10% for 50 road sections in the network, which is a significant improvement compared to traditional multi-vehicle average models. The errors of the SSL model are found to be significantly dependent on the iteration order. Based on the proposed model, the coupled impact of the sampling rate and vehicle quantity on the model’s accuracy is further discussed. The proposed approach provides new insights into large-scale pavement roughness measurements.

快速测量大型路面粗糙度一直是路面状况评估和维护中的热门话题。大多数传统方法依赖于专用设备，例如激光，激光雷达等，应在定制车辆上设置。随着传感技术的飞速发展，普通大众拥有的车辆可以自行收集振动测量值。该众包数据集方便，覆盖范围广，价格便宜，并且采样频率高，使其成为进行大规模路面粗糙度评估的合适来源。但是，由于受到隐私保护，这些数据缺少了车辆信息，这使得它们很难直接与传统的基于模型的方法一起使用。因此，在本文中，我们提出了一种半监督学习（SSL）模型来解决数据不完整和多车辆数据融合的问题。建立了使用车内振动对“国际粗糙度指数”（IRI）的数学推导。此外，考虑到多车场景，设计了一种自训练模型来迭代估计道路网络中的IRI。算法中考虑了车辆参数的置信度和IRI估计，以提高其可靠性和鲁棒性。建立了完整的汽车仿真模型以验证所提出模型的有效性。结果表明，网络中50个路段的总体相对误差小于10％，与传统的多车平均模型相比，这是一个重大改进。发现SSL模型的错误很大程度上取决于迭代顺序。基于提出的模型，进一步讨论了采样率和车辆数量对模型精度的耦合影响。所提出的方法为大规模路面粗糙度测量提供了新的见识。