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Deep Instance Segmentation with Automotive Radar Detection Points [article]

Jianan Liu, Weiyi Xiong, Liping Bai, Yuxuan Xia, Tao Huang, Wanli Ouyang, Bing Zhu
<span title="2022-04-18">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar.  ...  In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points.  ...  Automotive Radar-based Perception Automotive radar-based perception, including semantic segmentation, clustering, classification, instance segmentation, object detection, and tracking, has played an essential  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01775v6">arXiv:2110.01775v6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ryhi73z53bhdldmcglq7ucgz5m">fatcat:ryhi73z53bhdldmcglq7ucgz5m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424083138/https://arxiv.org/pdf/2110.01775v6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/28/01/28015e47cc615600db7df146f50d66e738fe750e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01775v6" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multi-View Radar Semantic Segmentation [article]

Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Florence Tupin, Julien Rebut
<span title="2021-08-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  Experiments conducted on the recent CARRADA dataset demonstrate that our best model outperforms alternative models, derived either from the semantic segmentation of natural images or from radar scene understanding  ...  Acknowledgements We thank Veronica Elizabeth Vargas Salas for her valuable help with temporal radar data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.16214v2">arXiv:2103.16214v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7dsyn6nfijflnp7hapzgeoilmq">fatcat:7dsyn6nfijflnp7hapzgeoilmq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210829181907/https://arxiv.org/pdf/2103.16214v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f5/66/f566c77cb52eae55404770c96860c7efbd67ffde.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.16214v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications [article]

Ole Schumann, Markus Hahn, Nicolas Scheiner, Fabio Weishaupt, Julius F. Tilly, Jürgen Dickmann, Christian Wöhler
<span title="2021-04-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented.  ...  Data provided by four series radar sensors mounted on one test vehicle were recorded and the individual detections of dynamic objects were manually grouped to clusters and labeled afterwards.  ...  Semantic Segmentation For semantic segmentation the same frame accumulation advises as for object detection apply.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.02493v1">arXiv:2104.02493v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eqiohyvbcvfvro4dk4amlfy2ce">fatcat:eqiohyvbcvfvro4dk4amlfy2ce</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210408071200/https://arxiv.org/pdf/2104.02493v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5d/e8/5de8507ec6e986da6a6c662e869fc725ed1104cf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.02493v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
<span title="2022-04-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  Semantic segmentation on radar representation has been less explored due to the lack of annotated datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.10646v3">arXiv:2112.10646v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pt32p4jwbzhlxdsf7bbjo6hute">fatcat:pt32p4jwbzhlxdsf7bbjo6hute</a> </span>
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Object detection for automotive radar point clouds – a comparison

Nicolas Scheiner, Florian Kraus, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
<span title="2021-11-16">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wkr3m5c25fed5foyzrqdes32aa" style="color: black;">AI Perspectives</a> </i> &nbsp;
All experiments are conducted using a conventional automotive radar system.  ...  However, most of them have not been compared to other methods or require next generation radar sensors which are far more advanced than current conventional automotive sensors.  ...  In semantic segmentation, a class label is predicted for each data point. The PointNet++ architecture is adapted to automotive radar data in [13] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s42467-021-00012-z">doi:10.1186/s42467-021-00012-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c4awtmqjsjb4dat3s3kh6ojc6y">fatcat:c4awtmqjsjb4dat3s3kh6ojc6y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220119180703/https://aiperspectives.springeropen.com/track/pdf/10.1186/s42467-021-00012-z.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/6e/d46ef432d3ee7b43bb949831d2d17e6a4d76b58f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s42467-021-00012-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation [article]

Weiyi Xiong, Jianan Liu, Yuxuan Xia, Tao Huang, Bing Zhu, Wei Xiang
<span title="2022-03-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving.  ...  To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation.  ...  Instance Segmentation with Automotive Radar Detection Points Due to the sparsity of automotive radar detection points, tasks such as instance segmentation with automotive radar detection points are more  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.06553v1">arXiv:2203.06553v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vifbvx2jxndn3c4pumjyy2fqla">fatcat:vifbvx2jxndn3c4pumjyy2fqla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220321121303/https://arxiv.org/pdf/2203.06553v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c9/49/c9496b2819730bee04f892bf198c599bebd70dda.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.06553v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications [article]

Christopher Grimm, Tai Fei, Ernst Warsitz, Ridha Farhoud, Tobias Breddermann, Reinhold Haeb-Umbach
<span title="2020-12-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate the framework in multiple applications like direction-of-arrival (DoA) estimation, target detection, semantic segmentation and estimation of radar power from camera data.  ...  In this paper, we present a novel framework to project automotive radar range-Doppler (RD) spectrum into camera image.  ...  Optimization For training of Ψ-Net, focal-loss [54] was evaluated between semantic segmentation prediction on RD-map projected into camera image Ψ and the semantic segmentation from teacher network Ψ  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.12809v1">arXiv:2012.12809v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lc76jzn2lvcbxos4gd2uq5kfoq">fatcat:lc76jzn2lvcbxos4gd2uq5kfoq</a> </span>
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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
<span title="2021-05-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  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.  ...  Section V details a baseline for radar semantic segmentation on raw representations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.01456v6">arXiv:2005.01456v6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c5xbynlzgbbtnigzz7peunah2i">fatcat:c5xbynlzgbbtnigzz7peunah2i</a> </span>
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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
<span title="2020-03-18">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  The radar data was collected using a special purpose sensor with much higher resolution and range than average industrial radar and is not designed based on the requirements of the automotive industry.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03449v1">arXiv:2004.03449v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/by37fu7uanesnc2gl33xfpcdbm">fatcat:by37fu7uanesnc2gl33xfpcdbm</a> </span>
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Deep learning for radar data exploitation of autonomous vehicle [article]

Arthur Ouaknine
<span title="2022-03-15">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This thesis then present a proposed set of deep learning architectures with their associated loss functions for RADAR semantic segmentation.  ...  This thesis focuses the on automotive RADAR, which is a low-cost active sensor measuring properties of surrounding objects, including their relative speed, and has the key advantage of not being impacted  ...  Sections 3.3 and 3.4 respectively review the related works on object detection and semantic segmentation applied to automotive RADAR.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.08038v1">arXiv:2203.08038v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zjupxkpaffgavm45oqpwnhkczq">fatcat:zjupxkpaffgavm45oqpwnhkczq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220317004701/https://arxiv.org/pdf/2203.08038v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/85/6e850dd99968805f7680d804fc40ce5474f5cd21.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.08038v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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
<span title="2021-03-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems.  ...  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.  ...  CONCLUSION In this paper, we proposed a novel deep model, Po-larNet, to segment open spaces in parking scenarios using automotive radar.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.03387v1">arXiv:2103.03387v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mwdttkalanbwxeypnp6x2iseva">fatcat:mwdttkalanbwxeypnp6x2iseva</a> </span>
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Image Segmentation and Region Classification in Automotive High-Resolution Radar Imagery

Yang Xiao, Liam Daniel, Marina Gashinova
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/blxwdtb2nfevpltlbto4vdqbte" style="color: black;">IEEE Sensors Journal</a> </i> &nbsp;
In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed  ...  Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving.  ...  radar map.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsen.2020.3043586">doi:10.1109/jsen.2020.3043586</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3cbtj2isazffvb6lgmxmgmdhz4">fatcat:3cbtj2isazffvb6lgmxmgmdhz4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715010036/https://ieeexplore.ieee.org/ielx7/7361/9347831/09288850.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a5/17/a517cf04bb0be6c767e801e36dfaef07f51be59c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsen.2020.3043586"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras [article]

Ciaran Eising, Jonathan Horgan, Senthil Yogamani
<span title="2021-11-11">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Surround-view camera systems typically comprise of four fisheye cameras with 190+ field of view covering the entire 360 around the vehicle focused on near-field sensing.  ...  When bounding box pedestrian detection was state of the art, before semantic and instance segmentation, most researchers in automotive pedestrian detection will have considered encoding a depth based on  ...  When there is an accurate localization, HD maps can be treated as a dominant cue, as a strong prior semantic segmentation is already available, and it can be refined by an online segmentation algorithm  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.17001v3">arXiv:2103.17001v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uyy2ieomf5cajhened27n3n2mm">fatcat:uyy2ieomf5cajhened27n3n2mm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211121081305/https://arxiv.org/pdf/2103.17001v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/18/fe1874f9f7a8495d04a374be41a1949eaa8a0277.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.17001v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation [article]

Liat Sless, Gilad Cohen, Bat El Shlomo, Shaul Oron
<span title="2019-09-02">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth.  ...  model used for occupancy grid mapping from clustered radar data.  ...  Lovasz loss was shown in the original paper to be useful for semantic segmentation learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.00415v2">arXiv:1904.00415v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ejilwnybvzedtgl42r36cz6spa">fatcat:ejilwnybvzedtgl42r36cz6spa</a> </span>
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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
<span title="2020-02-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.  ...  For example, Lombacher et al. employ Radar grid maps made by accumulating Radar data over several time-stamps [151] for static object classification [152] and semantic segmentation [153] in autonomous  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.07830v4">arXiv:1902.07830v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/or6enjxktnamdmh2yekejjr4re">fatcat:or6enjxktnamdmh2yekejjr4re</a> </span>
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