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.
@article{arxiv.1701.01272,
title = {Autoencoder Regularized Network For Driving Style Representation Learning},
author = {Weishan Dong and Ting Yuan and Kai Yang and Changsheng Li and Shilei Zhang},
journal= {arXiv preprint arXiv:1701.01272},
year = {2017}
}