Autoencoder Regularized Network For Driving Style Representation Learning

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.

Authors

• Weishan Dong
• Ting Yuan Chinese Academy of Science
• Kai Yang School of Computer Science and Technology, Tongji University, China
• Changsheng Li
• Shilei Zhang

Keywords

• Machine Learning: Deep Learning
• Machine Learning: Neural Networks
• Multidisciplinary Topics and Applications: AI and Ubiquitous Computing Systems