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Auteur Olivier Orfila |
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Affiner la recherche Interroger des sources externesTraffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)
Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse fonctionnelle (mathématiques)
[Termes IGN] apprentissage profond
[Termes IGN] carte routière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] détection d'objet
[Termes IGN] données routières
[Termes IGN] feu de circulation
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] signalisation routière
[Termes IGN] trace GPS
[Termes IGN] trafic routier
[Termes IGN] transformation en ondelettes
[Termes IGN] vitesseRésumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2020-336 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755
in International Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119[article]Documents numériques
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Titre : Using surrogate road network for map-matching: A sensitivity analysis of positional accuracy Type de document : Article/Communication Auteurs : Yann Méneroux Editeur : GeoComputation Conference Année de publication : 2017 Projets : 1-Pas de projet / Conférence : GeoComputation 2017, International conference on GeoComputation, Celebrating 21 Years of GeoComputation 04/09/2017 07/09/2017 Leeds Royaume-Uni open access proceedings Note générale : bibliographie Langues : Français (fre) Résumé : (auteur) Map-matching GPS traces on a reference road network often increases point positional accuracy, especially in urban environment where satellite signal is frequently obstructed by buildings. However, with receivers improvement, road data errors may no longer be ipso facto considered as negligible in front of GPS trace errors. This raises the question of sensitivity of map-matching output precision to input network geometric quality. We address the problem by attempting to relate gain in precision with a network quality index, based on two commonly used measures: averaged Hausdorff distance and areal difference. Analysis has been conducted by performing both GPS traces simulation and network perturbation. Results highlighted that the areal difference (for which we demonstrate that it can be considered as a distance) seems to be better related to map-matching output errors. We also provide an upper bound on the impact of using a surrogate reference network and we apply it to a practical case study. Numéro de notice : C2017-044 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91929 Documents numériques
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