# Feature extraction from telematics car driving heatmaps

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## Abstract

Insurance companies have started to collect high-frequency GPS car driving data to analyze the driving styles of their policyholders. In previous work, we have introduced speed and acceleration heatmaps. These heatmaps were categorized with the *K*-means algorithm to differentiate varying driving styles. In many situations it is useful to have low-dimensional continuous representations instead of unordered categories. In the present work we use singular value decomposition and bottleneck neural networks (autoencoders) for principal component analysis. We show that a two-dimensional representation is sufficient to re-construct the heatmaps with high accuracy (measured by Kullback–Leibler divergences).

## Keywords

Telematics car driving data Driving styles Unsupervised learning Pattern recognition Image recognition Bottleneck neural network Autoencoder Singular value decomposition Principal component analysis*K*-means algorithm Kullback–Leibler divergence

## Notes

### Acknowledgements

Guangyuan Gao: Financially supported by the Social Science Fund of China (Grant no. 16ZDA052) and MOE National Key Research Bases for Humanities and Social Sciences (Grant no. 16JJD910001).

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