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CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations

Abstract : 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 rangeangle-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.
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https://hal.telecom-paris.fr/hal-03324378
Contributor : Arthur Ouaknine Connect in order to contact the contributor
Submitted on : Monday, August 23, 2021 - 3:35:14 PM
Last modification on : Tuesday, October 19, 2021 - 11:15:17 AM

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Arthur Ouaknine, Alasdair Newson, Julien Rebut, Florence Tupin, Patrick Perez. CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations. 2020 25th International Conference on Pattern Recognition (ICPR), Jan 2021, Milan (virtual), Italy. ⟨10.1109/icpr48806.2021.9413181⟩. ⟨hal-03324378⟩

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