Development of machine learning assisted suspension vibration data-based road quality classification system

Roland Nagy, István Szalai

Abstract

<p>Vibrations in road vehicles can have highly harmful effects on both the vehicle components and the passengers. These vibrations are mainly caused by lowquality pavement, so it is important to keep the road network in good condition and to know its general quality. In our study, we present the development of a universally applicable, low-cost measurement system for the purpose of measuring the condition of pavement surfaces. The system can be used to identify road sections in urgent and near future need of maintenance, thus helping to schedule construction works efficiently. The system is based on an inertial sensor unit mounted on the vehicle suspension, in contrast to previous systems, and therefore offers an improvement in the accuracy of the measurement. In our study, a principal component analysis and time series segmentationbased algorithm is introduced to extract relevant features from the raw sensor data. Subsequently, each segment is classified into pre-defined classes based on its surface quality using a binary decision tree-based classification model fitted by supervised learning. After validation, the system is tested on public roads under real measurement conditions.</p>

Keywords

Road quality monitoring, Machine learning, IMU, Software sensor development, Principal component analysis, Decision tree, Road classification

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IMEKO-TC10-2023-007.pdf

DOI

10.21014/tc10-2023.007

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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