Tire-related crashes account for a large proportion of all types of car accidents. The causes of tire-related accidents are inappropriate tire temperature, pressure, and wear. Although temperature and pressure can be monitored easily with TPMS, there exists no system to monitor tire wear regularly. This paper proposes a system that can estimate tire wear using a 3-axis accelerometer attached to the tread inside the tire. This system utilizes axial acceleration, extracts feature from data acquired with the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several runs on a test track, it has been found that this system can estimate the tread depth with reasonable accuracy.

This paper presents the development of a magneto-optical encoder for higher precision and smaller size. In general, optical encoders can have very high precision based on the position information of the slate, while their sizes tend to be larger due to the presence of complex and large components, such as an optical module. In contrast, magnetic encoders have exactly the opposite characteristics, i.e., small size and low precision. In order to achieve encoder features encompassing the advantages of both optical and magnetic encoders, i.e., high precision and small size, we designed a magneto-optical encoder and developed a method to detect absolute position, by compensating for the error of the hall sensor using the linear table compensation method. The performance of the magneto-optical encoder was evaluated through an experimental testbed.