High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps
Joint Authors
Sotelo, Miguel A.
Fernández, Carlos
Muñoz-Bulnes, Jesús
Parra, Ignacio
García-Daza, Iván
Izquierdo, Rubén
Fernández Llorca, David
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-11
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Fernández, Carlos& Muñoz-Bulnes, Jesús& Fernández Llorca, David& Parra, Ignacio& García-Daza, Iván& Izquierdo, Rubén…[et al.]. 2018. High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1181023
Modern Language Association (MLA)
Fernández, Carlos…[et al.]. High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps. Journal of Advanced Transportation No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1181023
American Medical Association (AMA)
Fernández, Carlos& Muñoz-Bulnes, Jesús& Fernández Llorca, David& Parra, Ignacio& García-Daza, Iván& Izquierdo, Rubén…[et al.]. High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1181023
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181023