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SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services

Jelle De Bock (UGent) and Steven Verstockt (UGent)
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Abstract
<jats:p>Abstract. SmarterRoutes aims to improve navigational services and make them more dynamic and personalised by data-driven and environmentally-aware road scene complexity estimation. SmarterRoutes divides complexity into two subtypes: perceived and descriptive complexity. In the SmarterRoutes architecture, the overall road scene complexity is indicated by combining and merging parameters from both types of complexity. Descriptive complexity is derived from geospatial data sources, traffic data and sensor analysis. The architecture is currently using OpenStreetMap (OSM) tag analysis, Meten-In-Vlaanderen (MIV) derived traffic info and the Alaro weather model of the Royal Meteorological Institute of Belgium (RMI) as descriptive complexity indicators. For the perceived complexity an image based complexity estimation mechanism is presented. This image based Densenet Convolutional Neural Network (CNN) uses Street View images as input and was pretrained on buildings with Bag-of-Words and Structure-from-motion features. The model calculates an image descriptor allowing comparison of images by calculation of the Euclidean distances between descriptors. SmarterRoutes extends this model by additional hand-labelled rankings of road scene images to predict visual road complexity. The reuse of an existing pretrained model with an additional ranking mechanism produces results corresponding with subjective assessments of end-users. Finally, the global complexity mechanism combines the aforementioned sub-mechanisms and produces a service which should facilitate user-centred context-aware navigation by intelligent data selection and/or omission based on SmarterRoutes’ complexity input. </jats:p>

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Citation

Please use this url to cite or link to this publication:

MLA
De Bock, Jelle, and Steven Verstockt. “SmarterRoutes – Data-Driven Road Complexity Estimation for Level-of-Detail Adaptation of Navigation Services.” Advances in Cartography and GIScience of the ICA, 2019, pp. 1–8.
APA
De Bock, J., & Verstockt, S. (2019). SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services. Advances in Cartography and GIScience of the ICA, 1–8.
Chicago author-date
De Bock, Jelle, and Steven Verstockt. 2019. “SmarterRoutes – Data-Driven Road Complexity Estimation for Level-of-Detail Adaptation of Navigation Services.” Advances in Cartography and GIScience of the ICA, 1–8.
Chicago author-date (all authors)
De Bock, Jelle, and Steven Verstockt. 2019. “SmarterRoutes – Data-Driven Road Complexity Estimation for Level-of-Detail Adaptation of Navigation Services.” Advances in Cartography and GIScience of the ICA: 1–8.
Vancouver
1.
De Bock J, Verstockt S. SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services. Advances in Cartography and GIScience of the ICA. 2019;1–8.
IEEE
[1]
J. De Bock and S. Verstockt, “SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services,” Advances in Cartography and GIScience of the ICA, pp. 1–8, 2019.
@article{8635014,
  abstract     = {<jats:p>Abstract. SmarterRoutes aims to improve navigational services and make them more dynamic and personalised by data-driven and environmentally-aware road scene complexity estimation. SmarterRoutes divides complexity into two subtypes: perceived and descriptive complexity. In the SmarterRoutes architecture, the overall road scene complexity is indicated by combining and merging parameters from both types of complexity. Descriptive complexity is derived from geospatial data sources, traffic data and sensor analysis. The architecture is currently using OpenStreetMap (OSM) tag analysis, Meten-In-Vlaanderen (MIV) derived traffic info and the Alaro weather model of the Royal Meteorological Institute of Belgium (RMI) as descriptive complexity indicators. For the perceived complexity an image based complexity estimation mechanism is presented. This image based Densenet Convolutional Neural Network (CNN) uses Street View images as input and was pretrained on buildings with Bag-of-Words and Structure-from-motion features. The model calculates an image descriptor allowing comparison of images by calculation of the Euclidean distances between descriptors. SmarterRoutes extends this model by additional hand-labelled rankings of road scene images to predict visual road complexity. The reuse of an existing pretrained model with an additional ranking mechanism produces results corresponding with subjective assessments of end-users. Finally, the global complexity mechanism combines the aforementioned sub-mechanisms and produces a service which should facilitate user-centred context-aware navigation by intelligent data selection and/or omission based on SmarterRoutes’ complexity input.
                    </jats:p>},
  author       = {De Bock, Jelle and Verstockt, Steven},
  issn         = {2570-2084},
  journal      = {Advances in Cartography and GIScience of the ICA},
  language     = {eng},
  location     = {Vienna, Austria},
  pages        = {1--8},
  title        = {SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services},
  url          = {http://dx.doi.org/10.5194/ica-adv-2-2-2019},
  year         = {2019},
}

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