Tire Force Estimation in Intelligent Tires Using Machine Learning

TL;DR

This paper introduces an intelligent tire system utilizing a tri-axial accelerometer and machine learning techniques to accurately estimate tire forces in real-time, enhancing vehicle control and autonomous driving capabilities.

Contribution

It presents a novel integration of an accelerometer-based sensor system with neural network models for real-time tire force prediction, advancing intelligent tire technology.

Findings

Effective real-time tire force prediction achieved

Neural networks outperform other machine learning methods

System works under various driving conditions

Abstract

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.