Datasets created by the Institute of Automotive Engineering of TU Darmstadt
Tire Spray in Radar and Lidar Data
Simulation-based testing supports the challenging task of safety validation of automated driving functions. Virtual testing always entails the modeling of automotive perception sensors. These sensors are not only exposed to global weather conditions, like rain, fog, snow etc., but environmental influences also appear locally. Tire spray is one of the more challenging occurrences, because it involves other moving objects in the scenario. This data set is designed to systematically analyze the influence of tire spray on lidar and radar sensors. It consists of sensor measurements of two vehicle classes driving over asphalt with three water levels to differentiate multiple influence factors.
Perception Sensors in Adverse Weather
In the real world, automotive perception 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, but prior to modeling, they need to be systematically determined and assessed in the real world. This dataset consists of measurements in a static test setup over the course of 6 months in fall, winter and spring containing weather conditions like rain, snow, hail and fog in different intensities.
Testing of automated driving functions becomes more and more virtualized. A key part of this development is the implementation and validation of perception sensor models. With this dataset, radar cross-sections (RCS) of multiple objects with different aspect angles are examined. The experiment is set up as a slalom course to measure the RCS under realistic conditions in a real world environment.