Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest
| Autor: | Méneroux, Yann, Kanasugi, Hiroshi, Saint Pierre, Guillaume, Guilcher, Arnaud Le, Mustière, Sébastien, Shibasaki, Ryosuke, Kato, Yugo |
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| Přispěvatelé: | Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institute of Industrial Sciences (IIS), The University of Tokyo (UTokyo), Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - Equipe-projet STI (Cerema Equipe-projet STI), Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement (Cerema), Winter, Stephan and Griffin, Amy and Sester, Monika |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
[SPI]Engineering Sciences [physics]
000 Computer science knowledge general works 010504 meteorology & atmospheric sciences Computer Science 0211 other engineering and technologies 02 engineering and technology 01 natural sciences ComputingMilieux_MISCELLANEOUS 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| DOI: | 10.4230/lipics.giscience.2018.11 |
| Popis: | As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles. |
| Databáze: | OpenAIRE |
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