Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor. / Kortmann, Felix; Rodeheger, Malte; Warnecke, Alexander et al.
2020 IEEE 92nd Vehicular Technology Conference: Proceedings. Canada: IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. 9348664 (IEEE Vehicular Technology Conference).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Kortmann, F, Rodeheger, M, Warnecke, A, Meier, N, Heger, J, Funk, B & Drews, P 2020, Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor. in 2020 IEEE 92nd Vehicular Technology Conference: Proceedings., 9348664, IEEE Vehicular Technology Conference, IEEE - Institute of Electrical and Electronics Engineers Inc., Canada, 92nd IEEE Vehicular Technology Conference - VTC 2020, Virtual, Victoria, Canada, 04.10.20. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348664

APA

Kortmann, F., Rodeheger, M., Warnecke, A., Meier, N., Heger, J., Funk, B., & Drews, P. (2020). Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor. In 2020 IEEE 92nd Vehicular Technology Conference: Proceedings Article 9348664 (IEEE Vehicular Technology Conference). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348664

Vancouver

Kortmann F, Rodeheger M, Warnecke A, Meier N, Heger J, Funk B et al. Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor. In 2020 IEEE 92nd Vehicular Technology Conference: Proceedings. Canada: IEEE - Institute of Electrical and Electronics Engineers Inc. 2020. 9348664. (IEEE Vehicular Technology Conference). doi: 10.1109/VTC2020-Fall49728.2020.9348664

Bibtex

@inbook{d11adf93530e4dc4b45cee506e877581,
title = "Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor",
abstract = "In the past 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. Although, most of the information for the environment model are provided via in-vehicle generated data based on camera, LIDAR, and RADAR sensors, we propose a solution of classifying road quality within the spring-damper system of the vehicle. In this paper, we utilize the Vehicle Level Sensor (VLS), which is a standard component in modern vehicles, for road condition assessment. We present a simulation of the Quarter Vehicle Model (QVM) for road elevation measurement to enable each connected vehicle to provide valid data for a potential crowd sensing approach where every vehicle contributes data for past and consumes data for upcoming segments. The generated data is capable of providing the environment model with real-time data of upcoming road segments. The simulation results are validated on a test bench including a review of the errors.",
keywords = "Informatics, Business informatics",
author = "Felix Kortmann and Malte Rodeheger and Alexander Warnecke and Nicolas Meier and Jens Heger and Burkhardt Funk and Paul Drews",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/VTC2020-Fall49728.2020.9348664",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE 92nd Vehicular Technology Conference",
address = "United States",
note = "92nd IEEE Vehicular Technology Conference - VTC 2020 : Intelligent Mobile Connections , VTC2020 ; Conference date: 04-10-2020 Through 07-10-2020",
url = "https://events.vtsociety.org/vtc2020-fall/",

}

RIS

TY - CHAP

T1 - Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor

AU - Kortmann, Felix

AU - Rodeheger, Malte

AU - Warnecke, Alexander

AU - Meier, Nicolas

AU - Heger, Jens

AU - Funk, Burkhardt

AU - Drews, Paul

N1 - Conference code: 92

PY - 2020/11/1

Y1 - 2020/11/1

N2 - In the past 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. Although, most of the information for the environment model are provided via in-vehicle generated data based on camera, LIDAR, and RADAR sensors, we propose a solution of classifying road quality within the spring-damper system of the vehicle. In this paper, we utilize the Vehicle Level Sensor (VLS), which is a standard component in modern vehicles, for road condition assessment. We present a simulation of the Quarter Vehicle Model (QVM) for road elevation measurement to enable each connected vehicle to provide valid data for a potential crowd sensing approach where every vehicle contributes data for past and consumes data for upcoming segments. The generated data is capable of providing the environment model with real-time data of upcoming road segments. The simulation results are validated on a test bench including a review of the errors.

AB - In the past 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. Although, most of the information for the environment model are provided via in-vehicle generated data based on camera, LIDAR, and RADAR sensors, we propose a solution of classifying road quality within the spring-damper system of the vehicle. In this paper, we utilize the Vehicle Level Sensor (VLS), which is a standard component in modern vehicles, for road condition assessment. We present a simulation of the Quarter Vehicle Model (QVM) for road elevation measurement to enable each connected vehicle to provide valid data for a potential crowd sensing approach where every vehicle contributes data for past and consumes data for upcoming segments. The generated data is capable of providing the environment model with real-time data of upcoming road segments. The simulation results are validated on a test bench including a review of the errors.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85101398504&partnerID=8YFLogxK

U2 - 10.1109/VTC2020-Fall49728.2020.9348664

DO - 10.1109/VTC2020-Fall49728.2020.9348664

M3 - Article in conference proceedings

AN - SCOPUS:85101398504

T3 - IEEE Vehicular Technology Conference

BT - 2020 IEEE 92nd Vehicular Technology Conference

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Canada

T2 - 92nd IEEE Vehicular Technology Conference - VTC 2020

Y2 - 4 October 2020 through 7 October 2020

ER -