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Accurate estimation of the tire slip ratio is critical for vehicle safety, asit is necessary for vehicle control purposes. In this paper, an intelligenttire system is presented to develop a novel slip ratio estimation model usingmachine learning algorithms. The accelerations, generated by a triaxialaccelerometer installed onto the inner liner of the tire, are varied when thetire rotates to update the contact patch. Meanwhile, the slip ratio referencevalue can be measured by the MTS Flat-Trac tire test platform. Then, byanalyzing the variation between the accelerations and slip ratio, highly usefulfeatures are discovered, which are especially promising for assessing verticalacceleration. For these features, machine learning (ML) algorithms are trainedto build the slip ratio estimation model, in which the ML algorithms includeartificial neural networks (ANNs), gradient boosting machines (GBMs), randomforests (RFs), and support vector machines (SVMs). Finally, the estimated NRMSerrors are evaluated using 10-fold cross-validation (CV). The proposedestimation model is able to estimate the slip ratio continuously and stablyusing only the acceleration from the intelligent tire system, and the estimatedslip ratio range can reach 30%. The estimation results have high robustness tovehicle velocity and load, where the best NRMS errors can reach 4.88%. Insummary, the present study with the fusion of an intelligent tire system andmachine learning paves the way for the accurate estimation of the tire slipratio under different driving conditions, which create new opportunities forautonomous vehicles, intelligent tires, and tire slip ratio estimation.

A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning