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Communication Dans Un Congrès Année : 2020

Obstacle Detection based on Cooperative-Intelligent Transport System Data

Résumé

Cooperative Intelligent Systems development is growing and the data they produce is increasing exponentially. This amount of data will soon be large enough to fall in big data paradigm. We propose to exploit these data as data stream. We aim to detect anomaly on the road using concept drift detection methods over data stream. To achieve this purpose, we create a data generation tool to obtain large data-sets of vehicles taking an avoiding behavior and detect obstacles through crowdsensing. We use two scenarios that we aim to detect: a stopped car and a growing pothole. We focus our study on the vehicle orientation information on which we apply Page-Hinkley and ADWIN methods. We obtain interesting detection results with ADWIN on the stopped car scenario. The Page-Hinkley algorithm is obtaining good results but with a latency that makes it unexploitable in real context. But for the pothole detection, both approaches are not providing significant results.
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Dates et versions

hal-03039792 , version 1 (25-02-2022)

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Brice Leblanc, Hacene Fouchal, Cyril de Runz. Obstacle Detection based on Cooperative-Intelligent Transport System Data. IEEE Symposium on Computers and Communications (ISCC 2020), Jul 2020, Rennes, France. ⟨10.1109/ISCC50000.2020.9219629⟩. ⟨hal-03039792⟩
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