Road surface type and degradation contribute significantly to the rolling noise emission. In recent times, due to the innovation in vehicle propulsion, rolling noise also becomes a main factor in noise emission for lower order roads. Monitoring and labelling these roads, requires considerably
more e ort than monitoring primary roads and highways due to their large number. Therefore, we propose an opportunistic method where vehicles that are on the roads for other purposes, are used for rolling noise monitoring. The proposed method may also have some additional benefits over the
standard CPX regarding the distribution of tires used and the spread of typical driving speeds. However, measurement conditions are not as well knownand may influence the results obtained from individual vehicles significantly. The abundance of measurements data from many vehicles will nevertheless
allow to eliminate any modifiers and confounders. To that end, a machine learning cleaning algorithm inspired by denoising auto-encoders has been designed and implemented. This cleaning algorithm improves the convergence of measurements, giving the same quality of measurements with a lower
number of passages and cars on a road segment.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
October 12, 2020
More about this publication?
The INTER-NOISE and NOISE-CON congress and conference proceedings is a collection of the presented papers. The papers are not peer reviewed and usually represent a synopsis of the material presented at the congress or conference.
- Membership Information
- INCE Subject Classification
- Ingenta Connect is not responsible for the content or availability of external websites