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Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

Robert Prophet, Anastasios Deligiannis, Juan-Carlos Fuentes-Michel, Ingo Weber, Martin Vossiek
2020 IEEE Access  
Therefore, in this paper, we want to use 3D radar target lists and convert them into 3D grids, which are then transformed to SGs via CNN in order to compare the results with those of the 2D input grids  ...  Automotive radars that measure 3D are currently being developed. In particular, the resolution in elevation will improve significantly over the coming years.  ...  For this problem, the use of 3D input images would be indispensable. as a radar performance engineer for autonomous driving projects.  ... 
doi:10.1109/access.2020.3032034 fatcat:ft4wm73wsjcnjhevmskaq2r7zu

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

Liat Sless, Gilad Cohen, Bat El Shlomo, Shaul Oron
2019 arXiv   pre-print
model used for occupancy grid mapping from clustered radar data.  ...  The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth.  ...  Lovasz loss was shown in the original paper to be useful for semantic segmentation learning.  ... 
arXiv:1904.00415v2 fatcat:ejilwnybvzedtgl42r36cz6spa

Semantic Grid-Based Road Model Estimation for Autonomous Driving

Julian Thomas, Julian Tatsch, Wim van Ekeren, Raul Rojas, Alois Knoll
2019 2019 IEEE Intelligent Vehicles Symposium (IV)  
traffic participants are fused into semantic grids.  ...  Based on Dempster-Shafer theory and a novel frame of discernment, sensor measurements, such as lane markings, semantic segmentation of drivable and nondrivable areas and the trajectories of other observed  ...  In [15] CNNs are used to classify cells in a radar occupancy grid map into three classes (car, other, unlabeled).  ... 
doi:10.1109/ivs.2019.8813790 dblp:conf/ivs/ThomasTERK19 fatcat:rayo2toe6bdzngh3hqy2mgtqpi

Deep Instance Segmentation with High-Resolution Automotive Radar [article]

Jianan Liu, Weiyi Xiong, Liping Bai, Yuxuan Xia, Bing Zhu
2021 arXiv   pre-print
other is based on clustering of the radar detection points with semantic information.  ...  In this paper, we propose two efficient methods for instance segmentation with radar detection points, one is implemented in an end-to-end deep learning driven fashion using PointNet++ framework, and the  ...  For example, the radar detection points are used as input data for semantic segmentation task [23] , together with occupancy grid representation of environments [24, 25] .  ... 
arXiv:2110.01775v2 fatcat:ssglthojxvfqza7ctlxafwg3mu

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

Fahad Jibrin Abdu, Yixiong Zhang, Maozhong Fu, Yuhan Li, Zhenmiao Deng
2021 Sensors  
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.  ...  Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used.  ...  [50] proposed a new pipeline to segment both static and moving objects using automotive radar sensors for semantic (instance) segmentation applications.  ... 
doi:10.3390/s21061951 pmid:33802217 pmcid:PMC7999239 fatcat:4sek2e2parf2vpfatqhe7m5sjy

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.  ...  Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.  ...  Note that the inputs to our proposed models only include the radar data, and the output is generated as the occupancy grid map in Cartesian coordinates.  ... 
arXiv:2004.03449v1 fatcat:by37fu7uanesnc2gl33xfpcdbm

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
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.  ...  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  ...  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

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

Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez
2021 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.  ...  Radar point cloud segmentation has also been explored to estimate bird-eye-view occupancy grids, either for LD [22] , [39] or HD [34] , [33] , [37] radars.  ... 
arXiv:2112.10646v1 fatcat:lxhuoyduuvap3h2k5bptgmpxsa

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Timo Korthals, Mikkel Kragh, Peter Christiansen, Henrik Karstoft, Rasmus N. Jørgensen, Ulrich Rückert
2018 Frontiers in Robotics and AI  
Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant  ...  The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields.  ...  We acknowledge support for the Article Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.  ... 
doi:10.3389/frobt.2018.00028 pmid:33500915 pmcid:PMC7806069 fatcat:hp4krcsw3fazdeugmzypcnnwry

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.  ...  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.  ... 
arXiv:2103.16214v2 fatcat:7dsyn6nfijflnp7hapzgeoilmq

A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds

Alessandro Cennamo, Florian Kaestner, Anton Kummert
2021 Neural Processing Letters  
We introduce RadarPCNN, an architecture specifically designed for performing semantic segmentation on radar point clouds.  ...  Radar is a commonly adopted sensor in automotive industry, but its suitability to machine learning techniques still remains an open question.  ...  Without involving occupancy-grid maps, automotive radars provide poor information about stationary objects.  ... 
doi:10.1007/s11063-021-10544-4 fatcat:4mpngv7pfvenlodkcx65almlp4

Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning [article]

Abdelhak Loukkal
2020 arXiv   pre-print
Grid Maps (OGMs).  ...  Nonetheless, these camera-based networks reason in camera view where scale is not homogeneous and hence not directly suitable for motion forecasting.  ...  Related work Occupancy grid maps: Occupancy grid maps [11] represent the spatial environment of a robot and reflect its occupancy as a fine-grained metric grid.  ... 
arXiv:2008.04047v1 fatcat:qgv5b4rm55brbi67ymesk6l3lu

Automotive Radar — From First Efforts to Future Systems

Christian Waldschmidt, Juergen Hasch, Wolfgang Menzel
2021 IEEE Journal of Microwaves  
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  ...  radar.  ...  Furthermore, the use of a 2D-antenna characteristic also enables a 3D-occupancy grid map of the environment [109] .  ... 
doi:10.1109/jmw.2020.3033616 fatcat:yhe7goejznd75bqkeb6tiwzdry

Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar

Martin Dimitrievski, Ivana Shopovska, David Van Hamme, Peter Veelaert, Wilfried Philips
2020 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)  
Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception.  ...  The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar.  ...  Diagram of the proposed radar detector. Radar arrays are preprocessed and fed into a multi-resolution segmentation CNN producing a probability of occupancy grid.  ... 
doi:10.1109/itsc45102.2020.9294399 fatcat:vvlhnrysjjec3aajff2cb62fce

2D Car Detection in Radar Data with PointNets [article]

Andreas Danzer, Thomas Griebel, Martin Bach, Klaus Dietmayer
2019 arXiv   pre-print
To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box.  ...  This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets.  ...  We acknowledge the financial support for the project by the Federal Ministry of Education and Research of Germany (BMBF).  ... 
arXiv:1904.08414v2 fatcat:weji5tmchrdobewzdwav6t4taq
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