TY - JOUR

T1 - Modeling the Quarter-Vehicle

T2 - Use of Passive Sensor Data for Road Condition Monitoring

AU - Kortmann, Felix

AU - Horstkötter, Julin

AU - Warnecke, Alexander

AU - Meier, Nicolas

AU - Heger, Jens

AU - Funk, Burkhardt

AU - Drews, Paul

PY - 2021/7/15

Y1 - 2021/7/15

N2 - 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.

AB - 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.

KW - Business informatics

KW - roads

KW - sensors

KW - sensor systems

KW - rough surfaces

KW - wheels

KW - surface roughness

KW - smart phones

KW - digital service

KW - Environmental Monitoring

KW - measuring instruments

UR - http://www.scopus.com/inward/record.url?scp=85097937038&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2020.3042620

DO - 10.1109/JSEN.2020.3042620

M3 - Journal articles

VL - 21

SP - 15535

EP - 15543

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 14

M1 - 9281332

ER -