CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
This addresses the problem of limited data for radar-based perception in autonomous driving, though it is incremental as it builds on existing sensor fusion efforts.
The authors tackled the lack of annotated automotive radar datasets by introducing CARRADA, a dataset with synchronized camera and radar recordings and range-angle-Doppler annotations, and they provided a baseline radar semantic segmentation model evaluated on multiple metrics.
High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset are available online.