Name
Abstract | ||
|---|---|---|
This paper presents preliminary steps towards the development of a general spatial model, based on graph theory, to visualize and reason about the road traffic in an urban environment. This model includes qualitative, in addition to quantitative, data which improves its computation and makes it robust to quantitative errors. The paper also describes different levels of abstraction which define distinct points of view of the environment and hence, allow for the acquisition of heterogeneous data. The graph consists of set of entities and corresponding spatial relations, the semantics behind which differ according to the level of abstraction. The ideas presented in this paper bring together the research done by Geomatics and Robotics/Perception communities. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ITSC.2017.8317644 | 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) |
Keywords | Field | DocType |
urban environment,quantitative data,quantitative errors,heterogeneous data,qualitative spatial model,general spatial model,graph theory,spatial relations,road traffic visualization,abstraction level,road traffic reasoning | Graph theory,Spatial relation,Computer vision,Geomatics,Data mining,Abstraction,Artificial intelligence,Engineering,Perception,Semantics,Robotics,Computation | Conference |
ISSN | ISBN | Citations |
2153-0009 | 978-1-5386-1527-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamaldeep Singh Oberoi | 1 | 0 | 0.34 |
| Géraldine Del Mondo | 2 | 0 | 0.34 |
| Yohan Dupuis | 3 | 61 | 10.12 |
| Pascal Vasseur | 4 | 267 | 28.03 |