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Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors. | LitMetric

AI Article Synopsis

  • The publication discusses the challenges of safety validation for automated driving systems using simulation-based testing, focusing on how environmental conditions impact sensor performance.
  • It introduces a new data set that includes lidar data paired with synchronized measurements of weather conditions like fog, rain, snow, and sunlight from a long-term study.
  • The analysis reveals significant variations in sensor detection rates and performance based on different weather conditions, providing valuable insights for improving simulation models and sensor calibration.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315550PMC
http://dx.doi.org/10.3390/s22145266DOI Listing

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