Environmental Conditions in Lidar and Radar Data
| dc.contributor.author | Linnhoff, Clemens | |
| dc.contributor.author | Hofrichter, Kristof | |
| dc.contributor.author | Elster, Lukas | |
| dc.contributor.author | Rosenberger, Philipp | |
| dc.contributor.author | Winner, Hermann | |
| dc.date.accessioned | 2022-06-29T07:25:28Z | |
| dc.date.available | 2022-06-29T07:25:28Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022-06-29 | |
| dc.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 like rain, fog, snow etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced. 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 sun light. Next to the named funding projects, the dataset was also funded by VIVID, promoted by the German Federal Ministry of Education and Research, based on a decision of the Deutsche Bundestag. | de_DE |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3507 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-900 | |
| dc.rights.license | CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0) | |
| dc.subject | Automated Driving | de_DE |
| dc.subject | Lidar | de_DE |
| dc.subject | Radar | de_DE |
| dc.subject | Weather | de_DE |
| dc.subject | Fog | de_DE |
| dc.subject | Rain | de_DE |
| dc.subject | Snow | de_DE |
| dc.subject | Sun | de_DE |
| dc.subject | Perception | de_DE |
| dc.subject | Simulation | de_DE |
| dc.subject.classification | 4.41-04 | |
| dc.subject.ddc | 380 | |
| dc.title | Environmental Conditions in Lidar and Radar Data | de_DE |
| dc.type | Dataset | de_DE |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | 0000-0003-3309-0623 | |
| person.identifier.orcid | 0000-0002-9824-3195 | |
| tuda.history.classification | Version=2020-2024;407-04 Verkehrs- und Transportsysteme, Intelligenter und automatisierter Verkehr | |
| tuda.project | TÜV Rheinland | 19A19004E | SETLevel4to5 | |
| tuda.project | Bund/BMWi | 19A19002S | VVMethoden | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 20 of 47
| Name | Description | Size | Format | |
|---|---|---|---|---|
| snow.z01 | 4 GB | Unknown data format | ||
| snow.z02 | 4 GB | Unknown data format | ||
| snow.z03 | 4 GB | Unknown data format | ||
| snow.z04 | 4 GB | Unknown data format | ||
| snow.zip | 347.96 MB | ZIP-Archivdateien | ||
| sun.zip | 3.88 GB | ZIP-Archivdateien | ||
| rain.z01 | 4 GB | Unknown data format | ||
| rain.z02 | 4 GB | Unknown data format | ||
| rain.z03 | 4 GB | Unknown data format | ||
| rain.z04 | 4 GB | Unknown data format | ||
| rain.z05 | 4 GB | Unknown data format | ||
| rain.z06 | 4 GB | Unknown data format | ||
| rain.z07 | 4 GB | Unknown data format | ||
| rain.z08 | 4 GB | Unknown data format | ||
| rain.z09 | 4 GB | Unknown data format | ||
| rain.z10 | 4 GB | Unknown data format | ||
| rain.z11 | 4 GB | Unknown data format | ||
| rain.z12 | 4 GB | Unknown data format | ||
| rain.z13 | 4 GB | Unknown data format | ||
| rain.z14 | 4 GB | Unknown data format |