Year: 2022
Type: article
Abstract: Intelligent tires can be employed for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading as well as tread wear monitoring. In this paper we develop a measurement system for intelligent tires equipped with a 3-dimensional piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.
Authors: Tamas Dozsa, Janos Rado, Janos Volk, Adam Kisari, Alexandros Soumelidis, Peter Kovacs
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,195
OpenAlex ID: W4289535646