Urban road surface discrimination by tire-road noise analysis and data clustering

Ramos Romero, CA; Asensio, C; Moreno, R; de Arcas, G

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.

Citation

Ramos Romero, C., Asensio, C., Moreno, R., & de Arcas, G. (2022). Urban road surface discrimination by tire-road noise analysis and data clustering. Sensors, 22(24), 9686. https://doi.org/10.3390/s22249686

• Journal Article Type: Article
• Acceptance Date: Dec 9, 2022
• Online Publication Date: Dec 10, 2022
• Publication Date: Dec 10, 2022
• Deposit Date: Dec 16, 2022
• Publicly Available Date: Dec 16, 2022
• Journal: Sensors
• Publisher: MDPI
• Volume: 22
• Issue: 24
• Pages: 9686
• DOI: https://doi.org/10.3390/s22249686
• Publisher URL: https://doi.org/10.3390/s22249686
• Additional Information: Funders : Ecuadorian Government; Projects : “Convocatoria Abierta 2017” (SENESCYT)