Predicting the Wear Amount of Tire Tread Using 1D−CNN

Abstract

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.

Keywords

• tire wear prediction
• 1D−CNN
• bottleneck features
• tire internal acceleration
• tire internal pressure
• tire vertical load