Elster, Lukas ; Staab, Jan Philipp ; Peters, Steven (2023)
Making automotive radar sensor validation measurements comparable.
In: Applied Sciences, 13 (20)
doi: 10.3390/app132011405
Article, Bibliographie
Abstract
Virtual validation of radar sensor models is becoming increasingly important for the safety validation of Light Detection and Rangings (lidars). Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses Light Detection and Rangings (lidars) and the so-called Double Validation Metric (DVM) to effectively quantify deviations between distributions. By applying this metric, the study measures the reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real-world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behavior under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.
| Item Type: | Article |
|---|---|
| Erschienen: | 2023 |
| Creators: | Elster, Lukas ; Staab, Jan Philipp ; Peters, Steven |
| Type of entry: | Bibliographie |
| Title: | Making automotive radar sensor validation measurements comparable |
| Language: | English |
| Date: | 2023 |
| Publisher: | MDPI |
| Journal or Publication Title: | Applied Sciences |
| Volume of the journal: | 13 |
| Issue Number: | 20 |
| DOI: | 10.3390/app132011405 |
| Abstract: | Virtual validation of radar sensor models is becoming increasingly important for the safety validation of Light Detection and Rangings (lidars). Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses Light Detection and Rangings (lidars) and the so-called Double Validation Metric (DVM) to effectively quantify deviations between distributions. By applying this metric, the study measures the reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real-world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behavior under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models. |
| Uncontrolled Keywords: | automotive radar, validation measurements, virtual validation, sensor model validation, Double Validation Metric |
| Identification Number: | Artikel-ID: 11405 |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) > Driver Assistance |
| Date Deposited: | 23 May 2024 06:36 |
| Last Modified: | 23 May 2024 06:47 |
| PPN: | 518486923 |
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| Suche nach Titel in: | TUfind oder in Google |
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