@article{ref1,
title="Urban road surface discrimination by tire-road noise analysis and data clustering",
journal="Sensors (Basel)",
year="2022",
author="Ramos-Romero, Carlos and Asensio, César and Moreno, Ricardo and de Arcas, Guillermo",
volume="22",
number="24",
pages="e9686-e9686",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22249686",
url="http://dx.doi.org/10.3390/s22249686"
}