Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads
European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficienc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1254 |
| _version_ | 1797411867552907264 |
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| author | Olatz Iparraguirre Aiert Amundarain Alfonso Brazalez Diego Borro |
| author_facet | Olatz Iparraguirre Aiert Amundarain Alfonso Brazalez Diego Borro |
| author_sort | Olatz Iparraguirre |
| collection | DOAJ |
| description | European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system. |
| first_indexed | 2024-03-09T04:52:29Z |
| format | Article |
| id | doaj.art-f1346552816941faa101353565cc5f59 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:52:29Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f1346552816941faa101353565cc5f592023-12-03T13:09:28ZengMDPI AGSensors1424-82202021-02-01214125410.3390/s21041254Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative RoadsOlatz Iparraguirre0Aiert Amundarain1Alfonso Brazalez2Diego Borro3CEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, SpainCEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, SpainCEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, SpainCEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, SpainEuropean road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.https://www.mdpi.com/1424-8220/21/4/1254Cooperative Intelligent Transport Systems (C-ITS)traffic sign recognitionfog detectionintelligent roadsV2X communicationsroad maintenance |
| spellingShingle | Olatz Iparraguirre Aiert Amundarain Alfonso Brazalez Diego Borro Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads Sensors Cooperative Intelligent Transport Systems (C-ITS) traffic sign recognition fog detection intelligent roads V2X communications road maintenance |
| title | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads |
| title_full | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads |
| title_fullStr | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads |
| title_full_unstemmed | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads |
| title_short | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads |
| title_sort | sensors on the move onboard camera based real time traffic alerts paving the way for cooperative roads |
| topic | Cooperative Intelligent Transport Systems (C-ITS) traffic sign recognition fog detection intelligent roads V2X communications road maintenance |
| url | https://www.mdpi.com/1424-8220/21/4/1254 |
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