4 Search Results for "Garcia, Laurent"


Document
Micro- and Macroscopic Road Traffic Analysis using Drone Image Data

Authors: Friedrich Kruber, Eduardo Sánchez Morales, Robin Egolf, Jonas Wurst, Samarjit Chakraborty, and Michael Botsch

Published in: LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1


Abstract
The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis. In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro- and microscopic traffic analysis.

Cite as

Friedrich Kruber, Eduardo Sánchez Morales, Robin Egolf, Jonas Wurst, Samarjit Chakraborty, and Michael Botsch. Micro- and Macroscopic Road Traffic Analysis using Drone Image Data. In LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1, pp. 02:1-02:27, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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@Article{kruber_et_al:LITES.8.1.2,
  author =	{Kruber, Friedrich and S\'{a}nchez Morales, Eduardo and Egolf, Robin and Wurst, Jonas and Chakraborty, Samarjit and Botsch, Michael},
  title =	{{Micro- and Macroscopic Road Traffic Analysis using Drone Image Data}},
  booktitle =	{LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision},
  pages =	{02:1--02:27},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{1},
  editor =	{Kruber, Friedrich and S\'{a}nchez Morales, Eduardo and Egolf, Robin and Wurst, Jonas and Chakraborty, Samarjit and Botsch, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.1.2},
  doi =		{10.4230/LITES.8.1.2},
  annote =	{Keywords: traffic data analysis, trajectory data, drone image data}
}
Document
Human-Centred Feasibility Restoration

Authors: Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)


Abstract
Decision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and understand how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical and flexible user support. We evaluate the resulting framework on two real-world applications.

Cite as

Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda. Human-Centred Feasibility Restoration. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 49:1-49:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


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@InProceedings{senthooran_et_al:LIPIcs.CP.2021.49,
  author =	{Senthooran, Ilankaikone and Klapperstueck, Matthias and Belov, Gleb and Czauderna, Tobias and Leo, Kevin and Wallace, Mark and Wybrow, Michael and de la Banda, Maria Garcia},
  title =	{{Human-Centred Feasibility Restoration}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{49:1--49:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.49},
  URN =		{urn:nbn:de:0030-drops-153408},
  doi =		{10.4230/LIPIcs.CP.2021.49},
  annote =	{Keywords: Combinatorial optimisation, modelling, human-centred, conflict resolution, feasibility restoration, explainable AI, soft constraints}
}
Document
Justifications and Blocking Sets in a Rule-Based Answer Set Computation

Authors: Christopher Béatrix, Claire Lefèvre, Laurent Garcia, and Igor Stéphan

Published in: OASIcs, Volume 52, Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)


Abstract
Notions of justifications for logic programs under answer set semantics have been recently studied for atom-based approaches or argumentation approaches. The paper addresses the question in a rule-based answer set computation: the search algorithm does not guess on the truth or falsity of an atom but on the application or non application of a non monotonic rule. In this view, justifications are sets of ground rules with particular properties. Properties of these justifications are established; in particular the notion of blocking set (a reason incompatible with an answer set) is defined, that permits to explain computation failures. Backjumping, learning, debugging and explanations are possible applications.

Cite as

Christopher Béatrix, Claire Lefèvre, Laurent Garcia, and Igor Stéphan. Justifications and Blocking Sets in a Rule-Based Answer Set Computation. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 6:1-6:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)


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@InProceedings{beatrix_et_al:OASIcs.ICLP.2016.6,
  author =	{B\'{e}atrix, Christopher and Lef\`{e}vre, Claire and Garcia, Laurent and St\'{e}phan, Igor},
  title =	{{Justifications and Blocking Sets in a Rule-Based Answer Set Computation}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{6:1--6:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-007-1},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{52},
  editor =	{Carro, Manuel and King, Andy and Saeedloei, Neda and De Vos, Marina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2016.6},
  URN =		{urn:nbn:de:0030-drops-67310},
  doi =		{10.4230/OASIcs.ICLP.2016.6},
  annote =	{Keywords: Answer Set Programming, Justification, Rule-based Computation}
}
Document
Possibilistic Stable Models

Authors: Pascal Nicolas, Laurent Garcia, and Igor Stéphan

Published in: Dagstuhl Seminar Proceedings, Volume 5171, Nonmonotonic Reasoning, Answer Set Programming and Constraints (2005)


Abstract
We present the main lines of a new framework that we have defined in order to improve the knowledge representation power of Answer Set Programming paradigm. Our proposal is to use notions from possibility theory to extend the stable model semantics by taking into account a certainty level, expressed in terms of necessity measure, on each rule of a normal logic program. First of all, we introduce possibilistic definite logic programs and show how to compute the conclusions of such programs both in syntactic and semantic ways. The syntactic handling is done by help of a fix-point operator, the semantic part relies on a possibility distribution on all sets of atoms and the two approaches are shown to be equivalent. In a second part, we define what is a possibilistic stable model for a normal logic program, with default negation. Again, we define a possibility distribution allowing to determine the stable models. We end our presentation by showing how we can use our framework to adressing inconsistency in Answer Set Programming.

Cite as

Pascal Nicolas, Laurent Garcia, and Igor Stéphan. Possibilistic Stable Models. In Nonmonotonic Reasoning, Answer Set Programming and Constraints. Dagstuhl Seminar Proceedings, Volume 5171, pp. 1-6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2005)


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@InProceedings{nicolas_et_al:DagSemProc.05171.6,
  author =	{Nicolas, Pascal and Garcia, Laurent and St\'{e}phan, Igor},
  title =	{{Possibilistic Stable Models}},
  booktitle =	{Nonmonotonic Reasoning, Answer Set Programming and Constraints},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5171},
  editor =	{Gerhard Brewka and Ilkka Niemel\"{a} and Torsten Schaub and Miroslaw Truszczynski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05171.6},
  URN =		{urn:nbn:de:0030-drops-2641},
  doi =		{10.4230/DagSemProc.05171.6},
  annote =	{Keywords: Non monotonic reasoning, uncertainty, possibility theory}
}
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