  The concept of intelligent tires has drawn attention of researchers in the
areas of autonomous driving, advanced vehicle control, and artificial
intelligence. The focus of this paper is on intelligent tires and the
application of machine learning techniques to tire force estimation. We present
an intelligent tire system with a tri-axial acceleration sensor, which is
installed onto the inner liner of the tire, and Neural Network techniques for
real-time processing of the sensor data. The accelerometer is capable of
measuring the acceleration in x,y, and z directions. When the accelerometer
enters the tire contact patch, it starts generating signals until it fully
leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual
forces are measured. Signals generated by the accelerometer and MTS Flat-Trac
testing system are used for training three different machine learning
techniques with the purpose of online prediction of tire forces. It is shown
that the developed intelligent tire in conjunction with machine learning is
effective in accurate prediction of tire forces under different driving
conditions. The results presented in this work will open a new avenue of
research in the area of intelligent tires, vehicle systems, and tire force
estimation.
