ArticleSensors (Basel, Switzerland)2022
Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Impact of UK Weather on Autonomous Vehicle Radar, LiDAR and Camera Vision Systems.Sensors (Basel, Switzerland) · 2026Article
- HFSA-Net: A 3D Object Detection Network with Structural Encoding and Attention Enhancement for LiDAR Point Clouds.Sensors (Basel, Switzerland) · 2026Article
- Laser Pulse-Driven Multi-Sensor Time Synchronization Method for LiDAR Systems.Sensors (Basel, Switzerland) · 2025Article
- Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.IEEE transactions on bio-medical engineering · 2024Article
- Performance Verification of Autonomous Driving LiDAR Sensors under Rainfall Conditions in Darkroom.Sensors (Basel, Switzerland) · 2023Article
- A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors.Sensors (Basel, Switzerland) · 2023Review
- Camera-LiDAR Fusion Method with Feature Switch Layer for Object Detection Networks.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.