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Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test Drives into Simulation

Holder, Martin ; Elster, Lukas ; Winner, Hermann (2022):
Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test Drives into Simulation. (Publisher's Version)
In: Energies, 15 (3), MDPI, e-ISSN 1996-1073,
DOI: 10.26083/tuprints-00020520,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test Drives into Simulation
Language: English
Abstract:

In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the "ground truth" annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities for lidar and radar quantifiable in dynamic cases.

Journal or Publication Title: Energies
Volume of the journal: 15
Issue Number: 3
Place of Publication: Darmstadt
Publisher: MDPI
Collation: 16 Seiten
Uncontrolled Keywords: virtual validation, automated driving, ground truth, reference measurement, calibration method, simulation
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD)
Date Deposited: 13 Apr 2022 11:13
Last Modified: 21 Oct 2022 13:30
DOI: 10.26083/tuprints-00020520
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-205209
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20520
PPN: 500549176
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