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Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adver...

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Autores principales: Linnhoff, Clemens, Hofrichter, Kristof, Elster, Lukas, Rosenberger, Philipp, Winner, Hermann
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315550/
https://www.ncbi.nlm.nih.gov/pubmed/35890948
http://dx.doi.org/10.3390/s22145266
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author Linnhoff, Clemens
Hofrichter, Kristof
Elster, Lukas
Rosenberger, Philipp
Winner, Hermann
author_facet Linnhoff, Clemens
Hofrichter, Kristof
Elster, Lukas
Rosenberger, Philipp
Winner, Hermann
author_sort Linnhoff, Clemens
collection PubMed
description Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.
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spelling pubmed-93155502022-07-27 Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors Linnhoff, Clemens Hofrichter, Kristof Elster, Lukas Rosenberger, Philipp Winner, Hermann Sensors (Basel) Article Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set. MDPI 2022-07-14 /pmc/articles/PMC9315550/ /pubmed/35890948 http://dx.doi.org/10.3390/s22145266 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Linnhoff, Clemens
Hofrichter, Kristof
Elster, Lukas
Rosenberger, Philipp
Winner, Hermann
Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title_full Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title_fullStr Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title_full_unstemmed Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title_short Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
title_sort measuring the influence of environmental conditions on automotive lidar sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315550/
https://www.ncbi.nlm.nih.gov/pubmed/35890948
http://dx.doi.org/10.3390/s22145266
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