TY - GEN
T1 - CarraDA dataset
T2 - 25th International Conference on Pattern Recognition, ICPR 2020
AU - Ouaknine, Arthur
AU - Newson, Alasdair
AU - Rebut, Julien
AU - Tupin, Florence
AU - Pérez, Patrick
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
U2 - 10.1109/ICPR48806.2021.9413181
DO - 10.1109/ICPR48806.2021.9413181
M3 - Conference contribution
AN - SCOPUS:85104224801
T3 - Proceedings - International Conference on Pattern Recognition
SP - 5068
EP - 5075
BT - Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 January 2021 through 15 January 2021
ER -