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.
The dataset including all meta data is available for free from TUdatalib.
The tests where set up as a slalom course on the August-Euler-Airfield in Darmstadt, Germany. The slalom was set up in a reproducible manner by placing markers and pylons on the test track. The ego vehicle and the objects drove through the slalom at a constant speed. Each test drive contains 10 slalom periods and for each object 10 test drives were conducted.
The green pylons are gates through which the object passes and the orange cones are markers over which the object drives. The grey doted line represents the object's trajectory. The S-Class drives through the green gates in a straight line to measure different sensor azimuth and object yaw angles.
The ego vehicle was a Mercedes S-Class equipped with the following perception and reference sensors:
- 6 Continental ARS408 Radars
- 1 Velodyne VLP32C Lidar
- 1 Blickfeld Cube 1 Lidar
- 1 Ibeo LUX 2010 Lidar
- 1 Genesys ADMA G-Pro+ RTK-GNSS & IMU in each vehicle
Objects included in Dataset
If you find this data set useful for your research, please consider citing the publication:
L. Elster, M. F. Holder and M. Rapp, "A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation," in IEEE Sensors Journal, vol. 23, no. 5, pp. 4873-4882, 1 March1, 2023, doi: 10.1109/JSEN.2023.3238015
Former Research Associate