RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications

TL;DR

RadarScenes provides a comprehensive real-world radar point cloud dataset with detailed annotations, enabling the development and benchmarking of machine learning algorithms for automotive perception of moving objects.

Contribution

This paper introduces a new annotated radar dataset from four sensors, designed to facilitate the development of radar perception algorithms for automotive applications.

Findings

Dataset includes over four hours of driving data with manual annotations.

Proposals for standardized score calculation for object detection and classification.

Dataset available for download and further research on radar perception.

Abstract

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.