Autoencoder Regularized Network For Driving Style Representation Learning
Autoencoder Regularized Network For Driving Style Representation Learning
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DOI：
10.24963/ijcai.2017/222
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发表时间：
2017-01
影响因子：
3.6
通讯作者：
Weishan Dong;Ting Yuan;Kai Yang;Changsheng Li;Shilei Zhang
中科院分区：
文献类型：
--
作者：
Weishan Dong;Ting Yuan;Kai Yang;Changsheng Li;Shilei Zhang
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.
