TY - JOUR
T1 - Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces
AU - Haider, Arsalan
AU - Pigniczki, Marcell
AU - Köhler, Michael H.
AU - Fink, Maximilian
AU - Schardt, Michael
AU - Cichy, Yannik
AU - Zeh, Thomas
AU - Haas, Lukas
AU - Poguntke, Tim
AU - Jakobi, Martin
AU - Koch, Alexander W.
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - 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 (Formula presented.) of simulated and measured time domain signal amplitude is (Formula presented.). In addition, the (Formula presented.) of the number of points (Formula presented.) and mean intensity (Formula presented.) values received from the virtual and real targets are (Formula presented.) and (Formula presented.), respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points (Formula presented.) and mean intensity (Formula presented.) values up until now. Moreover, the distance error (Formula presented.) 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.
AB - 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 (Formula presented.) of simulated and measured time domain signal amplitude is (Formula presented.). In addition, the (Formula presented.) of the number of points (Formula presented.) and mean intensity (Formula presented.) values received from the virtual and real targets are (Formula presented.) and (Formula presented.), respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points (Formula presented.) and mean intensity (Formula presented.) values up until now. Moreover, the distance error (Formula presented.) 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.
KW - CarMaker
KW - advanced driver-assistance systems
KW - automotive LiDAR sensor
KW - co-simulation environment
KW - functional mock-up interface
KW - functional mock-up unit
KW - open simulation interface
KW - open standard
KW - point clouds
KW - proving ground
KW - silicon photomultipliers detector
KW - standardized interfaces
KW - time domain signal
UR - http://www.scopus.com/inward/record.url?scp=85139886363&partnerID=8YFLogxK
U2 - 10.3390/s22197556
DO - 10.3390/s22197556
M3 - Article
C2 - 36236655
AN - SCOPUS:85139886363
SN - 1424-8220
VL - 22
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 19
M1 - 7556
ER -