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Clustering driving styles via image processing

Published online by Cambridge University Press:  27 October 2020

Rui Zhu Open the ORCID record for Rui Zhu [Opens in a new window]  and
Mario V. Wüthrich
Rui Zhu*
Affiliation:
Faculty of Actuarial Science and Insurance, The Business School, City, University of London, London EC1Y 8TZ, UK
Mario V. Wüthrich
Affiliation:
RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland
*
*Corresponding author. E-mail: rui.zhu@city.ac.uk
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Abstract

It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.

Keywords

Telematics car driving dataDriving stylesUnsupervised learningImage processingTransfer learningAlexNet

Information

Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 276 - 290
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

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