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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6888 |
| _version_ | 1797585976359387136 |
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| author | Daniele Vignarca Stefano Arrigoni Edoardo Sabbioni |
| author_facet | Daniele Vignarca Stefano Arrigoni Edoardo Sabbioni |
| author_sort | Daniele Vignarca |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T00:16:38Z |
| format | Article |
| id | doaj.art-92591abdfa75405799b870dda3590aa8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:38Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-92591abdfa75405799b870dda3590aa82023-11-18T23:35:46ZengMDPI AGSensors1424-82202023-08-012315688810.3390/s23156888Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban ScenariosDaniele Vignarca0Stefano Arrigoni1Edoardo Sabbioni2Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, 20156 Milan, ItalyThe 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.https://www.mdpi.com/1424-8220/23/15/6888vehicle localizationKalman filterADASkinematic modelGPSTLA |
| spellingShingle | Daniele Vignarca Stefano Arrigoni Edoardo Sabbioni Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios Sensors vehicle localization Kalman filter ADAS kinematic model GPS TLA |
| 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 | vehicle localization Kalman filter ADAS kinematic model GPS TLA |
| url | https://www.mdpi.com/1424-8220/23/15/6888 |
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