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

A. Ouaknine, A. Newson, J. Rebut, F. Tupin, P. Pérez
2021 arXiv   pre-print
In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations.  ...  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.  ...  ACKNOWLEDGMENT The authors would like to express their thanks to the Sensor Cortex team, which has recorded these data and spent time to answer our questions, and to Gabriel de Marmiesse for his valuable  ... 
arXiv:2005.01456v6 fatcat:c5xbynlzgbbtnigzz7peunah2i

Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment [article]

Yizhou Wang, Gaoang Wang, Hung-Min Hsu, Hui Liu, Jenq-Neng Hwang
2021 arXiv   pre-print
To the best of our knowledge, CRUW is the first public large-scale dataset with a systematic annotation and evaluation system, which involves camera RGB images and radar RF images, collected in various  ...  In this paper, we propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system to address the radar object detection (ROD) task, which aims to classify and localize  ...  and annotation works.  ... 
arXiv:2105.05207v1 fatcat:5b5oz4co3nbbvllxmts6ri2xty

Multi-View Radar Semantic Segmentation [article]

Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Florence Tupin, Julien Rebut
2021 arXiv   pre-print
In this work, we propose several novel architectures, and their associated losses, which analyse multiple "views" of the range-angle-Doppler radar tensor to segment it semantically.  ...  However, they are seldom used for scene understanding due to the size and complexity of radar raw data and the lack of annotated datasets.  ...  Acknowledgements We thank Veronica Elizabeth Vargas Salas for her valuable help with temporal radar data.  ... 
arXiv:2103.16214v2 fatcat:7dsyn6nfijflnp7hapzgeoilmq

Deep learning for radar data exploitation of autonomous vehicle [article]

Arthur Ouaknine
2022 arXiv   pre-print
It will also describe the CARRADA dataset, composed of synchronised camera and RADAR data with a semi-automatic annotation method.  ...  Finally, this thesis exposes a collaborative contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR, LiDAR and camera.  ...  I would like to especially thank Domique Béréziat and Francesca Bovolo for reviewing my manuscript.  ... 
arXiv:2203.08038v1 fatcat:zjupxkpaffgavm45oqpwnhkczq

Beyond Point Clouds: A Knowledge-Aided High Resolution Imaging Radar Deep Detector for Autonomous Driving [article]

Ruxin Zheng, Shunqiao Sun, David Scharff, Teresa Wu
2021 arXiv   pre-print
Field experiments with multi-modal sensors were conducted at The University of Alabama. High resolution radar spectra were obtained and labeled using the camera and LiDAR recordings.  ...  resolution radar range-azimuth spectra for object detection and classification using deep neural networks.  ...  The radar angular resolution in CARRADA and CRUW datasets is larger than 10 • . The Astyx dataset is small and only contains sparse radar point clouds.  ... 
arXiv:2111.01246v1 fatcat:pce5svzviba77ebvp65jn7jh44

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges

Bernhard Vogginger, Felix Kreutz, Javier López-Randulfe, Chen Liu, Robin Dietrich, Hector A. Gonzalez, Daniel Scholz, Nico Reeb, Daniel Auge, Julian Hille, Muhammad Arsalan, Florian Mirus (+3 others)
2022 Frontiers in Neuroscience  
This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.  ...  For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead.  ...  The CARRADA dataset, on the other hand, includes the range-Doppler as well as the range-angle map for each scan.  ... 
doi:10.3389/fnins.2022.851774 pmid:35431782 pmcid:PMC9012531 fatcat:hejcc4cz2ndbbb4hbazycpxlju

Raw High-Definition Radar for Multi-Task Learning [article]

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
2022 arXiv   pre-print
In this paper, we propose a novel HD radar sensing model, FFT-RadNet, that eliminates the overhead of computing the range-azimuth-Doppler 3D tensor, learning instead to recover angles from a range-Doppler  ...  Also, we collected and annotated 2-hour worth of raw data from synchronized automotive-grade sensors (camera, laser, HD radar) in various environments (city street, highway, countryside road).  ...  As the radar signal is difficult to interpret by annotators and practitioners alike, a 16-layer automotive-grade laser scanner (LiDAR) and a 5 Mpix RGB camera are also provided.  ... 
arXiv:2112.10646v3 fatcat:pt32p4jwbzhlxdsf7bbjo6hute

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Fahad Jibrin Abdu, Yixiong Zhang, Maozhong Fu, Yuhan Li, Zhenmiao Deng
2021 Sensors  
Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used.  ...  This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object's range and radial velocity seamlessly.  ...  Recently, the authors of [44] presented a CARRADA dataset comprising a synchronized camera and low-level radar recordings (Range-Angle and Range-Doppler radar representations) to motivate deep learning  ... 
doi:10.3390/s21061951 pmid:33802217 pmcid:PMC7999239 fatcat:4sek2e2parf2vpfatqhe7m5sjy

Exploiting Temporal Relations on Radar Perception for Autonomous Driving [article]

Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu
2022 arXiv   pre-print
We consider the object recognition problem in autonomous driving using automotive radar sensors.  ...  To enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition.  ...  Boufounos, Toshiaki Koike-Akino, Hassan Mansour, and Philip V. Orlik for their helpful discussion.  ... 
arXiv:2204.01184v1 fatcat:xn5neqjxknavbkea5sepft6lny

Deep Instance Segmentation with Automotive Radar Detection Points [article]

Jianan Liu, Weiyi Xiong, Liping Bai, Yuxuan Xia, Tao Huang, Wanli Ouyang, Bing Zhu
2022 arXiv   pre-print
With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar.  ...  Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection  ...  As a result, RadarScenes [1] dataset is selected to validate the proposed methods. The dataset contains data from four front-mounted near-range automotive radars, one camera, and one odometer.  ... 
arXiv:2110.01775v6 fatcat:ryhi73z53bhdldmcglq7ucgz5m

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Dataset: Camera and Automotive Radar with Range- Angle-Doppler Annotations DAY 4 -Jan 15, 2021 Harsch, Lukas; Burgbacher, Johannes; Riedelbauch, Stefan 1402  ...  Sports Players 3D Localization with Identification Reasoning DAY 2 -Jan 13, 2021 Chen, Xin; Wang, Bin; Gao, Yongsheng 1589 Gaussian Convolution Angles: Invariant Vein and Texture Descriptors  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm