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Deep Open Space Segmentation using Automotive Radar [article]

Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Fahed Al Hassanat, Elnaz Jahani Heravi, Robert Laganiere, Julien Rebut, Waqas Malik
2020 arXiv   pre-print
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.  ...  A publically available dataset of radar observations called SCORP was collected. Deep models are evaluated with various radar input representations.  ...  Then, the annotated points are used to generate a mask for open-space segmentation. However, we confined the field-of-view of radar to 90°.  ... 
arXiv:2004.03449v1 fatcat:by37fu7uanesnc2gl33xfpcdbm

PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain [article]

Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Elnaz Jahani Heravi, Fahed Al Hassanat, Robert Laganiere, Julien Rebut, Waqas Malik
2021 arXiv   pre-print
In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations.  ...  Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems.  ...  CONCLUSION In this paper, we proposed a novel deep model, Po-larNet, to segment open spaces in parking scenarios using automotive radar.  ... 
arXiv:2103.03387v1 fatcat:mwdttkalanbwxeypnp6x2iseva

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

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
2022 arXiv   pre-print
FFT-RadNet is trained both to detect vehicles and to segment free driving space. On both tasks, it competes with the most recent radar-based models while requiring less compute and memory.  ...  With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades.  ...  Moreover, there is no previous work either on free driving space segmentation or semantic segmentation using only RD views of HD radar signals.  ... 
arXiv:2112.10646v3 fatcat:pt32p4jwbzhlxdsf7bbjo6hute

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
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  ...  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.  ...  A baseline for radar semantic segmentation is also proposed and evaluated on well-known metrics. We hope that it will encourage deep learning research applied to raw radar representations.  ... 
arXiv:2005.01456v6 fatcat:c5xbynlzgbbtnigzz7peunah2i

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

Arthur Ouaknine
2022 arXiv   pre-print
A deep learning architecture is also proposed to estimate the RADAR signal processing pipeline while performing multitask learning for object detection and free driving space segmentation.  ...  This thesis then present a proposed set of deep learning architectures with their associated loss functions for RADAR semantic segmentation.  ...  For these reasons, there was no open source RADAR dataset for automotive application before 2019, which has hampered research on deep learning applied to RADAR data.  ... 
arXiv:2203.08038v1 fatcat:zjupxkpaffgavm45oqpwnhkczq

Object detection for automotive radar point clouds – a comparison

Nicolas Scheiner, Florian Kraus, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
2021 AI Perspectives  
All experiments are conducted using a conventional automotive radar system.  ...  Recently, several new techniques for using machine learning algorithms towards the correct detection and classification of moving road users in automotive radar data have been introduced.  ...  Due to their high performance, deep CNN architectures have already made their way into automotive radar object detection.  ... 
doi:10.1186/s42467-021-00012-z fatcat:c4awtmqjsjb4dat3s3kh6ojc6y

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.  ...  We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models.  ...  The authors of [171] , presented a new automotive radar dataset named SCORP that can be applied to deep learning models for open space segmentation.  ... 
doi:10.3390/s21061951 pmid:33802217 pmcid:PMC7999239 fatcat:4sek2e2parf2vpfatqhe7m5sjy

Pointillism

Kshitiz Bansal, Keshav Rungta, Siyuan Zhu, Dinesh Bharadia
2020 Proceedings of the 18th Conference on Embedded Networked Sensor Systems  
The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions.  ...  We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds.  ...  Automotive Radar data processing using deep learning. Deep learning has been applied to various stages of radar data processing.  ... 
doi:10.1145/3384419.3430783 dblp:conf/sensys/BansalRZB20 fatcat:m3sk32a3bzdy3j22clwqnxdfyu

Automotive Radar — From First Efforts to Future Systems

Christian Waldschmidt, Juergen Hasch, Wolfgang Menzel
2021 IEEE Journal of Microwaves  
This opens up new research topics such as digital modulation schemes, radar networks, radar imaging, and machine learning.  ...  Although the beginning of research on automotive radar sensors goes back to the 1960s, automotive radar has remained one of the main drivers of innovation in millimeter wave technology over the past two  ...  Besides classification of specific targets, deep learning can provide better scene understanding by semantic segmentation [127] .  ... 
doi:10.1109/jmw.2020.3033616 fatcat:yhe7goejznd75bqkeb6tiwzdry

Multi-View Radar Semantic Segmentation [article]

Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Florence Tupin, Julien Rebut
2021 arXiv   pre-print
Fortunately, recent open-sourced datasets have opened up research on classification, object detection and semantic segmentation with raw radar signals using end-to-end trainable models.  ...  Automotive radars are low-cost active sensors that measure properties of surrounding objects, including their relative speed, and have the key advantage of not being impacted by rain, snow or fog.  ...  Acknowledgements We thank Veronica Elizabeth Vargas Salas for her valuable help with temporal radar data.  ... 
arXiv:2103.16214v2 fatcat:7dsyn6nfijflnp7hapzgeoilmq

RadarConf21 2021 Blank Page

2021 2021 IEEE Radar Conference (RadarConf21)  
of automotive radar -Algorithms for image formation and processing (segmentation and classification) -Beamforming for THz imagery (SAR, DBS, phased array) • Part III: Automotive MIMO Radar and MIMO Communications  ...  As radar and communication systems pose the greatest demand on spectrum access, their future designs must make use of all degrees-of-freedom (DoF): time, frequency, space, coding and polarization.  ...  The tutorial will follow the following schedule: Terahertz and Sub-Terahertz Automotive Radar: Emerging Technologies and Challenges [FA-5] Instructors Interest in bistatic and multistatic radar systems  ... 
doi:10.1109/radarconf2147009.2021.9454970 fatcat:gf7lve4dirh65jmkz7o4gzi754

Overview of the International Radar Symposium Best Papers, 2019, Ulm, Germany

István Balajti
2020 Repüléstudományi közlemények  
In civilian applications, the inter-radar interference of automotive radars is an emerging problem for automotive radar applications in case of dense deployment.  ...  Nowadays, the interference cancellation or mitigation plays a key important role in the effective use of the advanced radar technology.  ...  The designed antenna has been used in the composition of automotive radar of millimetre range.  ... 
doi:10.32560/rk.2019.3.553 fatcat:xwc6fasq7bah5j7tf7lctlpuki

Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation

Felix Nobis, Felix Fent, Johannes Betz, Markus Lienkamp
2021 Applied Sciences  
This paper proposes artificial neural network architectures to segment sparse radar point cloud data.  ...  Segmentation is an intermediate step towards radar object detection as a complementary concept to lidar object detection.  ...  Schumann [20] is the first to apply a pointnet-based approach to automotive radar data for semantic segmentation.  ... 
doi:10.3390/app11062599 fatcat:dh6y6lqdcfcwnb5y64slrfe7bi

Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges [article]

Di Feng, Christian Haase-Schuetz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck, Klaus Dietmayer
2020 arXiv   pre-print
This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.  ...  To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research.  ...  Each method uses different hardware, and the inference time is reported only by the authors. It is an open question how these methods perform when they are deployed on automotive hardware. C.  ... 
arXiv:1902.07830v4 fatcat:or6enjxktnamdmh2yekejjr4re

ICMIM 2020 TOC

2020 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)  
The frequency component coincides with the stride rate of the pedestrian. 10:07 Deep Open Space Segmentation using Automotive Radar In this work, we are proposing the use of radar with advanced deep segmentation  ...  models to identify open space in parking scenarios.  ...  In this paper, we report on a machine-learning approach to classify the blockage condition in automotive radar, using detection data.  ... 
doi:10.1109/icmim48759.2020.9299087 fatcat:3m4rbeocczemranjgyflevdyhi
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