Modeling the Quarter-Vehicle: Use of Passive Sensor Data for Road Condition Monitoring

Abstract

In recent years, automated driving has become one of the most important research fields in the automotive industry. A key component for a successful substitution of human driving by vehicles is a real-time model of the current environment including the traffic situation, the guide-way, and the road itself. We propose a solution for measuring road conditions within the spring-damper system of the vehicle. In this paper, we utilize a Vehicle Level Sensor (VLS) and an Acceleration Sensor (AS), both of which are standard components in modern vehicles, for road condition monitoring. Our model-based approach therefore consists purely of additional software. We present a calculation of the Quarter Vehicle Model (QVM) for road elevation measurements to enable each connected vehicle to provide valid data for a potential crowd-sensing approach, where every vehicle contributes past data and consumes data for upcoming segments. The generated data are capable of providing the environment model with real-time data. Our calculations are first validated in a laboratory setup, representing a down-scaled Quarter-Vehicle. The knowledge gained it then applied to a real vehicle. For this purpose, the measurement setup is explained, the model-based calculation and the parameters are adjusted, and the results are compared.