RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications
This dataset addresses the need for standardized radar data in automotive applications, facilitating algorithm development and evaluation, though it is incremental as it builds on existing data collection efforts.
The authors introduced RadarScenes, a real-world automotive radar dataset with over four hours of driving data and point-wise annotations, aimed at enabling the development of machine learning-based radar perception algorithms for moving road users.
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented. Data provided by four series radar sensors mounted on one test vehicle were recorded and the individual detections of dynamic objects were manually grouped to clusters and labeled afterwards. The purpose of this data set is to enable the development of novel (machine learning-based) radar perception algorithms with the focus on moving road users. Images of the recorded sequences were captured using a documentary camera. For the evaluation of future object detection and classification algorithms, proposals for score calculation are made so that researchers can evaluate their algorithms on a common basis. Additional information as well as download instructions can be found on the website of the data set: www.radar-scenes.com.