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Open AccessArticle

Convolutional Neural Network Classification of Telematics Car Driving Data

1
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China
2
RiskLab, Department of Mathematics, ETH Zurich, 8092 Zürich, Switzerland
*
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 21 December 2018 / Accepted: 9 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Insurance: Spatial and Network Data)
The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks. View Full-Text
Keywords: telematics car driving data; driving styles; pattern recognition; image recognition; convolutional neural networks telematics car driving data; driving styles; pattern recognition; image recognition; convolutional neural networks
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Gao, G.; Wüthrich, M.V. Convolutional Neural Network Classification of Telematics Car Driving Data. Risks 2019, 7, 6.

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