{"type":"ScholarlyArticle","title":"Autoencoder Regularized Network For Driving Style Representation Learning","authors":[{"name":"Weishan Dong","slug":"weishan-dong","given":"Weishan","family":"Dong","affiliation":"Baidu Research"},{"name":"Ting Yuan","slug":"ting-yuan","given":"Ting","family":"Yuan","affiliation":"Civil Aviation Management Institute of China"},{"name":"Kai Yang","slug":"kai-yang","given":"Kai","family":"Yang","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Changsheng Li","slug":"changsheng-li","given":"Changsheng","family":"Li","affiliation":"University of Electronic Science and Technology of China"},{"name":"Shilei Zhang","slug":"shilei-zhang","given":"Shilei","family":"Zhang","affiliation":"IBM Research - China"}],"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.","doi":"10.24963/ijcai.2017/222","url":"https://ijcai.org/proceedings/2017/222","pdf":"https://ijcai.org/proceedings/2017/0222.pdf","markdown":"https://ijcai.org/proceedings/2017/222.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/222","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":222,"pages":{"first":1603,"last":1609},"keywords":["Machine Learning: Neural Networks","Multidisciplinary Topics and Applications: AI and Ubiquitous Computing Systems","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Argyriou et al., 2007] Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 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