ABSTRACT

Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance of the distress, which makes it difficult to assess its depth or impact on passengers’ experience. This paper proposes a data fusion-based method for pavement distress evaluation, which comprehensively considers the joint effect of distress physical appearance and the corresponding impact on riding comfort. A deep convolutional neural network was employed to automatically detect and locate the pavement distress using image data. A wavelet transform was applied to extract the acceleration effectuated by the defects in the frequency domain using vibration data. Finally, a comfort evaluation index was constructed based on the results of image and vibration data fusion. Furthermore, a mobile vehicle-mounted collective system was designed for rapid evaluation of the pavement distress, which integrated multiple distributed accelerometers, an industrial camera, and a graphics processing unit. The results demonstrated the stability and efficiency of the proposed approach, making it a potential tool to comprehensively evaluate the condition of pavement distress.

摘要

路面状况的评估在路面维护和提高驾驶舒适性方面发挥着重要作用。目前的评估方法主要根据遇险的几何外观采用人工权重，这使得难以评估其深度或对乘客体验的影响。本文提出一种基于数据融合的路面遇险评价方法，综合考虑遇险物理外观的联合效应及其对乘坐舒适性的影响。采用深度卷积神经网络使用图像数据自动检测和定位路面故障。应用小波变换，利用振动数据在频域中提取由缺陷引起的加速度。最后，基于图像和振动数据融合结果构建舒适度评价指标。此外，还设计了一个用于快速评估路面状况的移动车载集体系统，该系统集成了多个分布式加速度计、一个工业相机和一个图形处理单元。结果证明了所提出方法的稳定性和效率，使其成为综合评估路面破损状况的潜在工具。