  Accurate estimation of the tire slip ratio is critical for vehicle safety, as
it is necessary for vehicle control purposes. In this paper, an intelligent
tire system is presented to develop a novel slip ratio estimation model using
machine learning algorithms. The accelerations, generated by a triaxial
accelerometer installed onto the inner liner of the tire, are varied when the
tire rotates to update the contact patch. Meanwhile, the slip ratio reference
value can be measured by the MTS Flat-Trac tire test platform. Then, by
analyzing the variation between the accelerations and slip ratio, highly useful
features are discovered, which are especially promising for assessing vertical
acceleration. For these features, machine learning (ML) algorithms are trained
to build the slip ratio estimation model, in which the ML algorithms include
artificial neural networks (ANNs), gradient boosting machines (GBMs), random
forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS
errors are evaluated using 10-fold cross-validation (CV). The proposed
estimation model is able to estimate the slip ratio continuously and stably
using only the acceleration from the intelligent tire system, and the estimated
slip ratio range can reach 30%. The estimation results have high robustness to
vehicle velocity and load, where the best NRMS errors can reach 4.88%. In
summary, the present study with the fusion of an intelligent tire system and
machine learning paves the way for the accurate estimation of the tire slip
ratio under different driving conditions, which create new opportunities for
autonomous vehicles, intelligent tires, and tire slip ratio estimation.
