Abstract：
The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.
摘要：
道路的表面状况对与运输技术相关的各种过程具有直接影响，道路设施质量,道路安全,和交通噪音排放。为检测路面状况而开发的方法对于维护和修复计划至关重要。也与自动运输系统和电子移动解决方案的驾驶环境检测相关。在本文中,提出了轮胎道路噪声排放特征的聚类，以检测自然驾驶事件期间轮迹区域的状况。这种基于声学的方法在不间断的现实交通状况下应用于城市地区。使用所提出的方法，在轮径相互作用区的检查路线上，可以识别至少两组表面状态。更新车道的市区检出率达到75％，不良车道的检出率达到72％。
