Abnormal Road Surface Detection Using Wheel Sensor Data

TL;DR

This paper presents a novel intelligent tire measurement system using a 3-D force sensor and adaptive Hermite functions to detect abnormal road surfaces with high accuracy, combining analytical and machine learning methods.

Contribution

It introduces a new tire-based measurement system with feature extraction and classification algorithms for abnormal road surface detection.

Findings

Achieved over 97% mean accuracy in detecting road abnormalities.

Developed both analytical and machine learning algorithms for classification.

Validated the system with real-world measurement data.

Abstract

Intelligent tires can be used for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading, and tread wear monitoring. In this article, we develop a measurement system for intelligent tires equipped with a 3-D piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.