  Intelligent tires can be used for a wide array of applications ranging from
tire pressure monitoring to analyzing tire/road interactions, wheel loading,
and tread wear monitoring. In this article, we develop a measurement system for
intelligent tires equipped with a 3-D 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%.
