- Automated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate theAutomated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate the motion distortion effect modeling. The mean absolute percentage error (MAPE), the occupied cell ratio (OCR), and the Barons cross-correlation coefficient (BCC) are used to quantify the correlation between the virtual and the real LiDAR point cloud data. The results show that the deterministic approach matches the real measurements better than the analytical approach for the scenarios in which the yaw rate of the ego vehicle changes rapidly.…
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MetadatenAuthor: | Arsalan HaiderORCiD, Lukas HaasORCiDGND, Shotaro Koyama, Lukas Elster, Michael H. Köhler, Michael Schardt, Thomas ZehORCiDGND, Hideo Inoue, Martin Jakobi, Alexander W. Koch |
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DOI: | https://doi.org/10.1109/ACCESS.2024.3355066 |
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Identifier: | 2169-3536 OPAC HS OPAC extern |
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Parent Title (English): | IEEE access |
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Subtitle (English): | practical research, open solutions |
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Publisher: | IEEE |
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Place of publication: | New York |
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Document Type: | Article |
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Language: | English |
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Date of Publication (online): | 2024/01/17 |
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Year of first Publication: | 2024 |
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Tag: | LiDAR sensor; false negative; false positive; functional mock-up interface; functional mock-up unit; highly automated driving; motion distortion; open simulation interface; point cloud distortion; simulation-based testing |
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Volume: | 2024 |
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Number of pages: | 17 Seiten |
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First Page: | 1 |
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Last Page: | 17 |
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Institutes: | Fakultät Elektrotechnik |
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| IFM - Institut für Fahrerassistenz und vernetzte Mobilität |
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Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften |
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Open Access: | open_access |
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Research focus: | FSP2: Mobilität |
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Publication Lists: | Zeh, Thomas |
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| Haider, Arsalan |
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| Haas, Lukas |
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Publication reviewed: | begutachtet |
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Release Date: | 2024/01/29 |
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