Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems
This work addresses the need for reliable tire-road interaction data in automotive engineering, but it is incremental as it applies an existing machine learning method to a specific sensor dataset.
The researchers tackled the problem of predicting lateral tire forces for vehicle control by developing a Gaussian Process Regression model using data from an accelerometer-based intelligent tire, achieving acceptable accuracy and providing uncertainty estimates useful for control strategies.
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.