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arXiv · 1701.01272

Autoencoder Regularized Network For Driving Style Representation Learning

Weishan Dong·Ting Yuan·Kai Yang·Changsheng Li·Shilei Zhang
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Abstract

In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods.

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Weishan Dong, Ting Yuan, Kai Yang, Changsheng Li, Shilei Zhang. 2017-01-05. Autoencoder Regularized Network For Driving Style Representation Learning. https://arxiv.org/abs/1701.01272

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