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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 functi...

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Detalles Bibliográficos
Autores principales: Haider, Arsalan, Pigniczki, Marcell, Köhler, Michael H., Fink, Maximilian, Schardt, Michael, Cichy, Yannik, Zeh, Thomas, Haas, Lukas, Poguntke, Tim, Jakobi, Martin, Koch, Alexander W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572647/
https://www.ncbi.nlm.nih.gov/pubmed/36236655
http://dx.doi.org/10.3390/s22197556
Descripción
Sumario: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: see text] of simulated and measured time domain signal amplitude is [Formula: see text]. In addition, the [Formula: see text] of the number of points [Formula: see text] and mean intensity [Formula: see text] values received from the virtual and real targets are [Formula: see text] and [Formula: see text] , respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points [Formula: see text] and mean intensity [Formula: see text] values up until now. Moreover, the distance error [Formula: see text] 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.