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Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

  • This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error (MAPE) of simulated and measured time domain signal amplitude is 1.7%. In addition, the MAPE of the number of points Npoints and mean intensity Imean values received from the virtual and real targets are 8.5% and 9.3%, respectively. To the author’sThis work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error (MAPE) of simulated and measured time domain signal amplitude is 1.7%. In addition, the MAPE of the number of points Npoints and mean intensity Imean values received from the virtual and real targets are 8.5% and 9.3%, respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points Npoints and mean intensity Imean values up until now. Moreover, the distance error derror is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.show moreshow less

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Metadaten
Author:Arsalan HaiderORCiD, Marcell Pigniczki, Michael H. Köhler, Maximilian Fink, Michael Schardt, Yannik Cichy, Thomas ZehORCiDGND, Lukas HaasORCiDGND, Tim PoguntkeGND, Martin Jakobi, Alexander W. Koch
DOI:https://doi.org/10.3390/s22197556
Identifier:1424-8220 OPAC HS OPAC extern
Parent Title (English):Sensors
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2022/10/05
Year of first Publication:2022
Volume:2022
Issue:19
Article Number:7556
Number of pages:23 Seiten
First Page:1
Last Page:23
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Poguntke, Tim
Zeh, Thomas
Pigniczki, Marcell
Haas, Lukas
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2023/01/23
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