All datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us and we will immediately remove the respective data from our server. Only a small subset is published here. If you need access to the whole dataset, please feel free to contact us at automotive @oth-aw.de via email. Two paper belong to the upload. The first one explaining the data labeling process was already published within the ICECCME 2022 (https://ieeexplore.ieee.org/document/9988075), the other one explaining the dataset in detail will be published soon. A preprint of the second paper is already available at TechRxiv (The KI-ASIC Dataset, https://www.techrxiv.org/articles/preprint/The_KI-ASIC_Dataset/22680358, status from 2023-05-28). The recorded sensor data can be displayed using the exemplary scripts at https://gitlab.com/oth-aw_automotive/scripts-for-ki-asic-dataset.

We present a novel dataset captured from a BMW X5 test carrier within the German research project KI-ASIC for use in radar sensor development and autonomous driving research. Our work aims at providing a blueprint for the process of creating labeled datasets for the development of neural networks for pattern recognition in radar data in the automotive environment. With a variety of different sensor types such as wide angle color cameras, a high-resolution color stereo camera, an Ouster OS1-64 laser scanner with gradient beam distribution and three novel Infineon radar sensors, we recorded over 100,000 scenes of real traffic scenarios as well as defined test scenarios with a frequency of 10 Hz. The scenarios in real traffic contain inner-city situations, but also scenes from rural areas with static and dynamic objects. Besides, the defined test scenarios are based on the NCAP scenarios and focus mostly on turning, overtaking and follow-up maneuvers. The data from the different sensors is calibrated, synchronized and timestamped including raw and rectified information. Our dataset also contains labels for all detected objects from a defined class list with distance and angle properties.