Tire Slip Angle Estimation based on the Intelligent Tire Technology

TL;DR

This paper introduces a novel method combining intelligent tire technology and machine learning to accurately estimate tire slip angles, enhancing vehicle safety and control in extreme driving conditions.

Contribution

It presents a new approach integrating MEMS sensors and machine learning for precise slip angle estimation, improving vehicle dynamics analysis.

Findings

Machine learning techniques accurately estimate slip angles up to 10 degrees.

Frequency domain analysis enhances estimation accuracy.

The method enables better vehicle control and safety in extreme maneuvers.

Abstract

Tire slip angle is a vital parameter in tire/vehicle dynamics and control. This paper proposes an accurate estimation method by the fusion of intelligent tire technology and machine-learning techniques. The intelligent tire is equipped by MEMS accelerometers attached to its inner liner. First, we describe the intelligent tire system along with the implemented testing apparatus. Second, experimental results under different loading and velocity conditions are provided. Then, we show the procedure of data processing, which will be used for training three different machine learning techniques to estimate tire slip angles. The results show that the machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. More importantly, with the accurate tire slip angle estimation, all other states and parameters can be easily and precisely obtained, which is significant to vehicle advanced control, and thus this study has a high potential to obviously improve the vehicle safety especially in extreme maneuvers.