Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

Abstract

The accurate detection of tire tread wear plays an important role in preventing tire-related accidents. In previous studies, tire wear detection is performed by interpreting mathematical models and tire characteristics. However, this approach may not accurately reflect the real driving environment. In this study, we propose a tire tread wear detection system that utilizes machine learning to provide accurate results under real-road driving conditions. The proposed system comprises: 1) an intelligent tire that samples the measured acceleration signals and processes them in a dataset; 2) a preprocessing component that extracts features from the collected data according to the degree of wear; and 3) a detection component that uses a deep neural network to classify the degree of wear. To implement the proposed system in a vehicle, we designed an acceleration-based intelligent tire that can transmit data over wireless networks. At speeds between 30 and 80 km/h, the proposed system was experimentally demonstrated to achieve an accuracy of 95.51% for detecting tire tread wear under real-road driving conditions. Moreover, this system uses only preprocessed acceleration signals and machine-learning algorithms, without requiring complex physical models and numerical analyses.