Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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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/works › Article in conference proceedings › Research › peer-review
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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 -