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Communication Dans Un Congrès Année : 2020

C-ITS data completion to improve unsupervised driving profile detection

Résumé

Connected vehicles is a growing field of research that will produce great amount of data in near future. These data can be mined to generate traffic prediction, detect driver profile, find alternative route, etc.. This will help car manufacturers, road operators, telecom operators and other actors in the sector to improve road safety and drivers comfort. But nowadays few data are collected to create these tools.In this paper we compare different completion approach on data extracted from real experimentation on road to perform efficient driving profile detection. We analyze the deviations of the driver headings along a defined trajectory on specific Points of Interest (POI) to extract the driving profiles.
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Dates et versions

hal-02885746 , version 1 (25-02-2022)

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Brice Leblanc, Secil Ercan, Cyril De Runz. C-ITS data completion to improve unsupervised driving profile detection. 91st IEEE Vehicular Technology Conference (VTC2020-Spring), May 2020, Antwerp, Belgium. ⟨10.1109/VTC2020-Spring48590.2020.9128731⟩. ⟨hal-02885746⟩
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