Datasets created by the Institute of Automotive Engineering of TU Darmstadt
Perception Sensors in Adverse Weather
Simulation-based testing supports the challenging task of safety validation of automated driving functions. Virtual testing always entails modeling of automotive perception sensors. 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, but prior to modeling, they need to be systematically assessed in the real world. This data set 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, fog and direct sun light in different intensities.
Road Spray in Radar and Lidar Data
Simulation-based testing supports the challenging task of safety validation of automated driving functions. Virtual testing always entails modeling of automotive perception sensors. These sensors are not only exposed to weather conditions like rain, fog, snow etc., but environmental influences are also caused by other traffic participants. Road spray is a prominent example of such conditions. This data set is designed to systematically analyze the influence of road 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.
RCS Measurement under Real Driving Conditions
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.