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

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Modeling the Quarter-Vehicle: Use of Passive Sensor Data for Road Condition Monitoring. / Kortmann, Felix; Horstkötter, Julin; Warnecke, Alexander et al.
In: IEEE Sensors Journal, Vol. 21, No. 14, 9281332, 15.07.2021, p. 15535-15543.

Research output: Journal contributionsJournal articlesResearchpeer-review

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APA

Vancouver

Kortmann F, Horstkötter J, Warnecke A, Meier N, Heger J, Funk B et al. Modeling the Quarter-Vehicle: Use of Passive Sensor Data for Road Condition Monitoring. IEEE Sensors Journal. 2021 Jul 15;21(14):15535-15543. 9281332. doi: 10.1109/JSEN.2020.3042620

Bibtex

@article{a06a705541bd44afbb469109b2bd8584,
title = "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.",
keywords = "Business informatics, roads, sensors, sensor systems, rough surfaces, wheels, surface roughness, smart phones, digital service, Environmental Monitoring, measuring instruments",
author = "Felix Kortmann and Julin Horstk{\"o}tter and Alexander Warnecke and Nicolas Meier and Jens Heger and Burkhardt Funk and Paul Drews",
year = "2021",
month = jul,
day = "15",
doi = "10.1109/JSEN.2020.3042620",
language = "English",
volume = "21",
pages = "15535--15543",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
number = "14",

}

RIS

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 -