Semantic Segmentation on 3D Occupancy Grids for Automotive Radar
Radar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle. Moreover, numerous successes have already been achieved regarding the classification of such objects. However, the advantage...
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doaj-69598b8709054280b994727b4ebcaa172021-03-30T04:15:57ZengIEEEIEEE Access2169-35362020-01-01819791719793010.1109/ACCESS.2020.30320349229096Semantic Segmentation on 3D Occupancy Grids for Automotive RadarRobert Prophet0https://orcid.org/0000-0003-1212-1697Anastasios Deligiannis1https://orcid.org/0000-0003-0651-3926Juan-Carlos Fuentes-Michel2https://orcid.org/0000-0003-1017-2730Ingo Weber3Martin Vossiek4https://orcid.org/0000-0002-8369-345XInstitute of Microwaves and Photonics, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, GermanyBMW Group, Munich, GermanyBMW Group, Munich, GermanyBMW Group, Munich, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, GermanyRadar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle. Moreover, numerous successes have already been achieved regarding the classification of such objects. However, the advantage of instantaneous velocity measurement is lost when detecting static objects, so that their classification is much more demanding. In this paper, we use semantic segmentation networks to distinguish between frequently occurring infrastructure objects. The resulting semantic grids provide a location-based classification of the vehicle environment. Since even modern radars have a significantly poorer angular resolution than lidars, the relatively thin radar point cloud is accumulated in advance and transformed into 2D or 3D grids that act as network inputs. Occupancy grids are particularly advantageous here, since they calculate not only the obstacles but also the free spaces. With suitable parameter selection, which is very challenging due to the complexity of radar measurement, the resulting grids allow for good association with camera images. Finally, in order to evaluate possible advantages of 3D grids as network input with respect to the segmentation result, we created and evaluated a simulation dataset and two different real-world datasets in car parks and on motorways. As a result, Jaccard coefficients between 81% and 88% were achieved, depending on the dataset. It was also found that 3D input images lead to improvements in the car park dataset.https://ieeexplore.ieee.org/document/9229096/79 GHzautomotive radardeep learningoccupancy gridsemantic segmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Robert Prophet Anastasios Deligiannis Juan-Carlos Fuentes-Michel Ingo Weber Martin Vossiek |
spellingShingle |
Robert Prophet Anastasios Deligiannis Juan-Carlos Fuentes-Michel Ingo Weber Martin Vossiek Semantic Segmentation on 3D Occupancy Grids for Automotive Radar IEEE Access 79 GHz automotive radar deep learning occupancy grid semantic segmentation |
author_facet |
Robert Prophet Anastasios Deligiannis Juan-Carlos Fuentes-Michel Ingo Weber Martin Vossiek |
author_sort |
Robert Prophet |
title |
Semantic Segmentation on 3D Occupancy Grids for Automotive Radar |
title_short |
Semantic Segmentation on 3D Occupancy Grids for Automotive Radar |
title_full |
Semantic Segmentation on 3D Occupancy Grids for Automotive Radar |
title_fullStr |
Semantic Segmentation on 3D Occupancy Grids for Automotive Radar |
title_full_unstemmed |
Semantic Segmentation on 3D Occupancy Grids for Automotive Radar |
title_sort |
semantic segmentation on 3d occupancy grids for automotive radar |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Radar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle. Moreover, numerous successes have already been achieved regarding the classification of such objects. However, the advantage of instantaneous velocity measurement is lost when detecting static objects, so that their classification is much more demanding. In this paper, we use semantic segmentation networks to distinguish between frequently occurring infrastructure objects. The resulting semantic grids provide a location-based classification of the vehicle environment. Since even modern radars have a significantly poorer angular resolution than lidars, the relatively thin radar point cloud is accumulated in advance and transformed into 2D or 3D grids that act as network inputs. Occupancy grids are particularly advantageous here, since they calculate not only the obstacles but also the free spaces. With suitable parameter selection, which is very challenging due to the complexity of radar measurement, the resulting grids allow for good association with camera images. Finally, in order to evaluate possible advantages of 3D grids as network input with respect to the segmentation result, we created and evaluated a simulation dataset and two different real-world datasets in car parks and on motorways. As a result, Jaccard coefficients between 81% and 88% were achieved, depending on the dataset. It was also found that 3D input images lead to improvements in the car park dataset. |
topic |
79 GHz automotive radar deep learning occupancy grid semantic segmentation |
url |
https://ieeexplore.ieee.org/document/9229096/ |
work_keys_str_mv |
AT robertprophet semanticsegmentationon3doccupancygridsforautomotiveradar AT anastasiosdeligiannis semanticsegmentationon3doccupancygridsforautomotiveradar AT juancarlosfuentesmichel semanticsegmentationon3doccupancygridsforautomotiveradar AT ingoweber semanticsegmentationon3doccupancygridsforautomotiveradar AT martinvossiek semanticsegmentationon3doccupancygridsforautomotiveradar |
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