Road-Side Unit Anomaly Detection
Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and it...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Vehicles |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-8921/5/4/80 |
| _version_ | 1827573326651850752 |
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| author | Mohamed-Lamine Benzagouta Hasnaâ Aniss Hacène Fouchal Nour-Eddin El Faouzi |
| author_facet | Mohamed-Lamine Benzagouta Hasnaâ Aniss Hacène Fouchal Nour-Eddin El Faouzi |
| author_sort | Mohamed-Lamine Benzagouta |
| collection | DOAJ |
| description | Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required. |
| first_indexed | 2024-03-08T20:17:45Z |
| format | Article |
| id | doaj.art-0e668216432241699d9feb72ed7d2ee5 |
| institution | Directory Open Access Journal |
| issn | 2624-8921 |
| language | English |
| last_indexed | 2024-03-08T20:17:45Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vehicles |
| spelling | doaj.art-0e668216432241699d9feb72ed7d2ee52023-12-22T14:47:49ZengMDPI AGVehicles2624-89212023-10-01541467148110.3390/vehicles5040080Road-Side Unit Anomaly DetectionMohamed-Lamine Benzagouta0Hasnaâ Aniss1Hacène Fouchal2Nour-Eddin El Faouzi3ERENA, Département COSYS, Université Gustave Eiffel, 33400 Bordeaux, FranceERENA, Département COSYS, Université Gustave Eiffel, 33400 Bordeaux, FranceLab-I*, Université de Reims Champagne-Ardenne, 51097 Reims, FranceENTPE, Université de Lyon, F-69675 Lyon, FranceActors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.https://www.mdpi.com/2624-8921/5/4/80C-ITSfailure detectionroad-side unitdata analysismachine learning |
| spellingShingle | Mohamed-Lamine Benzagouta Hasnaâ Aniss Hacène Fouchal Nour-Eddin El Faouzi Road-Side Unit Anomaly Detection Vehicles C-ITS failure detection road-side unit data analysis machine learning |
| title | Road-Side Unit Anomaly Detection |
| title_full | Road-Side Unit Anomaly Detection |
| title_fullStr | Road-Side Unit Anomaly Detection |
| title_full_unstemmed | Road-Side Unit Anomaly Detection |
| title_short | Road-Side Unit Anomaly Detection |
| title_sort | road side unit anomaly detection |
| topic | C-ITS failure detection road-side unit data analysis machine learning |
| url | https://www.mdpi.com/2624-8921/5/4/80 |
| work_keys_str_mv | AT mohamedlaminebenzagouta roadsideunitanomalydetection AT hasnaaaniss roadsideunitanomalydetection AT hacenefouchal roadsideunitanomalydetection AT noureddinelfaouzi roadsideunitanomalydetection |