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Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles
For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285307/ https://www.ncbi.nlm.nih.gov/pubmed/32456152 http://dx.doi.org/10.3390/s20102961 |
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author | Viktorović, Miloš Yang, Dujuan de Vries, Bauke |
author_facet | Viktorović, Miloš Yang, Dujuan de Vries, Bauke |
author_sort | Viktorović, Miloš |
collection | PubMed |
description | For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed. |
format | Online Article Text |
id | pubmed-7285307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72853072020-06-15 Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles Viktorović, Miloš Yang, Dujuan de Vries, Bauke Sensors (Basel) Article For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed. MDPI 2020-05-23 /pmc/articles/PMC7285307/ /pubmed/32456152 http://dx.doi.org/10.3390/s20102961 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Viktorović, Miloš Yang, Dujuan de Vries, Bauke Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title | Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title_full | Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title_fullStr | Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title_full_unstemmed | Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title_short | Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles |
title_sort | connected traffic data ontology (ctdo) for intelligent urban traffic systems focused on connected (semi) autonomous vehicles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285307/ https://www.ncbi.nlm.nih.gov/pubmed/32456152 http://dx.doi.org/10.3390/s20102961 |
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