Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
Open accessAbstract
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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Rain and snow have a significant impact on detection accuracy because water droplets and snowflakes can distort light under certain conditions, confusing the sensors and causing them to transmit less and less accurate data to the data processing system.
“Rain and snow have a significant impact on detection accuracy because water droplets and snowflakes can distort light under certain conditions, confusing the sensors and causing them to transmit less and less accurate data to the data processing system.”
From Investigating the Impact of Environmental Factors on Autonomous Vehicle Sensors (2025) · cites this paper as [8]
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- Authors
- Linnhoff, C., Hofrichter, K., Elster, L., Rosenberger, P., & Winner, H.
- Affiliations
- Technische Universität Darmstadt (DE)
- Corresponding author
- Linnhoff, Clemens
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-07-14
- Volume
- 22
- Issue
- 14
- Pages
- 5266
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9315550
- OpenAlex
- W4285595616
- Cited by
- 44
- References
- 23
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 10 · 2023: 10 · 2022: 1
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Funders
- SET Level and VVM of the PEGASUS project family
- German Federal Ministry for Economic Affairs and Energy and VIVID
- German Federal Ministry of Education and Research
- Deutsche Forschungsgemeinschaft (DFG—German Research Foundation)
- Open Access Publishing Fund of Technical University of Darmstadt
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Physics and Astronomy › Instrumentation › Advanced Optical Sensing Technologies
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- automotive LiDAR, environmental factors, fog, rain, snow, sunlight, atmospheric attenuation, range distribution, detection rate, stochastic modeling, sensor modeling, simulation-based testing
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