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Detection of road pavement quality using statistical clustering methods

机译：利用统计聚类方法检测道路路面质量

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Road owners are concerned with the state of the road surface and they try to reduce noise coming from the road as much as possible. Using sound level measuring equipment installed inside a car, we can indirectly measure the road pavement state. Noise inside a car is made up of rolling noise, engine noise and other confounding factors. Rolling noise is influenced by noise modifiers such as car speed, acceleration, temperature and road humidity. Engine noise is influenced by car speed, acceleration, and gear shifts. Techniques need to be developed which compensate for these modifying factors and filter out the confounding noise. This paper presents a hierarchical clustering method resulting in a mapping of the road pavement quality. We present the method using a dataset recorded in multiple cars under different circumstances. The data has been retrieved by placing a Raspberry Pi device within these cars and recording the sound and location during various trips at different times. The sound data of our dataset was then corrected for correlation with speed and acceleration. Furthermore, clustering techniques were used in order to estimate the type and condition of the pavement using this set of noise measurements. The algorithms were run on a small dataset and compared to a ground truth which was derived from visual observations. The results were best for a combination of Generalised Additive Model (GAM) correction on the data combined with hierarchical clustering. A connectivity matrix merging points close to each other further enhances the results for road pavement quality detection, and results in a road type detection rate around 90%.
机译：道路所有者涉及道路表面的状态，他们尽可能地减少来自道路的噪音。使用安装在汽车内的声级测量设备，我们可以间接测量道路路面状态。汽车内部的噪音由滚动噪音，发动机噪音和其他混杂因素组成。滚动噪声受噪声改性剂的影响，如汽车速度，加速度，温度和道路湿度。发动机噪声受汽车速度，加速度和换档的影响。需要开发技术，该技术可以补偿这些修改因子并过滤滤除混杂噪声。本文提出了一种分层聚类方法，从而产生了道路路面质量的映射。我们在不同情况下使用多辆车中记录的数据集提供了该方法。通过将覆盆子PI器件放置在这些汽车内并在不同时间进行各种旅行期间记录声音和位置来检索数据。然后纠正我们数据集的声音数据以与速度和加速相关。此外，使用聚类技术以估计使用该噪声测量的路面的类型和条件。该算法在小型数据集上运行，并与来自视觉观测的地面真理进行比较。结果最适合与分层聚类相结合的数据上的广义添加剂模型（GAM）校正的组合。彼此接近的连接矩阵合并点进一步增强了道路路面质量检测的结果，并导致道路型检测率约为90％。

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