Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems

TL;DR

This paper develops a Gaussian Process Regression model to predict lateral forces in intelligent tires, enabling reliable tire-road interaction data for advanced vehicle control systems, especially at high slip angles.

Contribution

It introduces a novel GPR-based lateral force prediction model for intelligent tires, enhancing accuracy and uncertainty estimation over existing methods.

Findings

GPR model predicts lateral forces with acceptable accuracy.

The system provides reliable tire-road interaction data at high slip angles.

Uncertainty estimates from GPR aid vehicle control design.

Abstract

Understanding the dynamic behavior of tires and their interactions with road plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about the tire-road interactions through tire embedded sensors is very demanding for developing enhanced vehicle control systems. Thus, the main objectives of the present research work are i. to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles; and ii. to develop a lateral force predictor based on a machine learning tool, more specifically the Gaussian Process Regression (GPR) technique. It is delineated that the proposed intelligent tire system can provide reliable information about the tire-road interactions even in the case of high slip angles. Besides, the lateral forces model based on GPR can predict forces with acceptable accuracy and provide level of uncertainties that can be very useful for designing vehicle control strategies.