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Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering.
Ramos-Romero, Carlos; Asensio, César; Moreno, Ricardo; de Arcas, Guillermo.
Affiliation
  • Ramos-Romero C; Acoustics Research Centre, University of Salford, Manchester M5 4WT, UK.
  • Asensio C; ETSI Sistemas de Telecomunicación, Departamento de Ingeniería Audiovisual y Comunicaciones, Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), Universidad Politécnica de Madrid, 28031 Madrid, Spain.
  • Moreno R; Institute for Chemical-Physical Processes of the Italian Research Council (CNR-IPCF), Via Giuseppe Moruzzi 1, 56124 Pisa, Italy.
  • de Arcas G; ETSI Sistemas de Telecomunicación, Departamento de Ingeniería Audiovisual y Comunicaciones, Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), Universidad Politécnica de Madrid, 28031 Madrid, Spain.
Sensors (Basel) ; 22(24)2022 Dec 10.
Article in En | MEDLINE | ID: mdl-36560056
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Automobile Driving / Noise, Transportation Type of study: Prognostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: Reino Unido

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Automobile Driving / Noise, Transportation Type of study: Prognostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: Reino Unido