RADIATE (RAdar Dataset In Adverse weaThEr) is a high-resolution radar dataset which includes about 3 hours annotated radar images and more than 200K labelled instances on public roads. It focuses on multi-modal sensor data (radar, camera, 3D LiDAR and GPS/IMU) in adverse weather conditions, such as dense fog and heavy snowfall. It aims to facilitate research on object detection, tracking, Simultaneous Localization and Mapping (SLAM) and scene understanding using radar sensing in extreme weathers.
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Paper
Please cite the following paper if you use RADIATE. Thanks.
Marcel Sheeny, Emanuele De Pellegrin, Mukherjee Saptarshi, Alireza Ahrabian, Sen Wang and Andrew Wallace. RADIATE: A Radar Dataset for Automotive Perception. arXiv:2010.09076, 2020.
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[BibTex]
@article{sheeny2020radiate,
title={RADIATE: A Radar Dataset for Automotive Perception},
author={Sheeny, Marcel and De Pellegrin, Emanuele and Mukherjee, Saptarshi and Ahrabian, Alireza and Wang, Sen and Wallace, Andrew},
journal={arXiv preprint arXiv:2010.09076},
year={2020}
}
Acknowledgement
This work was supported by Jaguar Land Rover and the UK Engineering and Physical Sciences Research Council, through TASCC: Pervasive low-Tera Hz and Video Sensing for Car Autonomy and Driver Assistance project (Grant reference EP/N012402/1).
This work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
