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Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios
The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422591/ https://www.ncbi.nlm.nih.gov/pubmed/37571669 http://dx.doi.org/10.3390/s23156888 |
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author | Vignarca, Daniele Arrigoni, Stefano Sabbioni, Edoardo |
author_facet | Vignarca, Daniele Arrigoni, Stefano Sabbioni, Edoardo |
author_sort | Vignarca, Daniele |
collection | PubMed |
description | The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving. |
format | Online Article Text |
id | pubmed-10422591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104225912023-08-13 Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios Vignarca, Daniele Arrigoni, Stefano Sabbioni, Edoardo Sensors (Basel) Article The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving. MDPI 2023-08-03 /pmc/articles/PMC10422591/ /pubmed/37571669 http://dx.doi.org/10.3390/s23156888 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vignarca, Daniele Arrigoni, Stefano Sabbioni, Edoardo Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title | Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title_full | Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title_fullStr | Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title_full_unstemmed | Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title_short | Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios |
title_sort | vehicle localization kalman filtering for traffic light advisor application in urban scenarios |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422591/ https://www.ncbi.nlm.nih.gov/pubmed/37571669 http://dx.doi.org/10.3390/s23156888 |
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