High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps
This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/2096970 |
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| _version_ | 1832556640091504640 |
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| author | Carlos Fernández Jesús Muñoz-Bulnes David Fernández-Llorca Ignacio Parra Iván García-Daza Rubén Izquierdo Miguel Á. Sotelo |
| author_facet | Carlos Fernández Jesús Muñoz-Bulnes David Fernández-Llorca Ignacio Parra Iván García-Daza Rubén Izquierdo Miguel Á. Sotelo |
| author_sort | Carlos Fernández |
| collection | DOAJ |
| description | This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems. |
| format | Article |
| id | doaj-art-e09df49eea2d49e189183cd42247b383 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e09df49eea2d49e189183cd42247b3832025-02-03T05:44:49ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/20969702096970High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation MapsCarlos Fernández0Jesús Muñoz-Bulnes1David Fernández-Llorca2Ignacio Parra3Iván García-Daza4Rubén Izquierdo5Miguel Á. Sotelo6Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanyALTRAN Innovation, Madrid, SpainComputer Engineering Department, University of Alcalá, Madrid, SpainComputer Engineering Department, University of Alcalá, Madrid, SpainComputer Engineering Department, University of Alcalá, Madrid, SpainComputer Engineering Department, University of Alcalá, Madrid, SpainComputer Engineering Department, University of Alcalá, Madrid, SpainThis paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems.http://dx.doi.org/10.1155/2018/2096970 |
| spellingShingle | Carlos Fernández Jesús Muñoz-Bulnes David Fernández-Llorca Ignacio Parra Iván García-Daza Rubén Izquierdo Miguel Á. Sotelo High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps Journal of Advanced Transportation |
| title | High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps |
| title_full | High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps |
| title_fullStr | High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps |
| title_full_unstemmed | High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps |
| title_short | High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps |
| title_sort | high level interpretation of urban road maps fusing deep learning based pixelwise scene segmentation and digital navigation maps |
| url | http://dx.doi.org/10.1155/2018/2096970 |
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