{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/autoencoder-regularized-network-for-driving","title":"Autoencoder Regularized Network For Driving Style Representation Learning","arxiv_id":"1701.01272","date":"2017-01-05","proceeding":null,"authors":["Weishan Dong","Ting Yuan","Kai Yang","Changsheng Li","Shilei Zhang"],"abstract":"In this paper, we study learning generalized driving style representations\nfrom automobile GPS trip data. We propose a novel Autoencoder Regularized deep\nneural Network (ARNet) and a trip encoding framework trip2vec to learn drivers'\ndriving styles directly from GPS records, by combining supervised and\nunsupervised feature learning in a unified architecture. Experiments on a\nchallenging driver number estimation problem and the driver identification\nproblem show that ARNet can learn a good generalized driving style\nrepresentation: It significantly outperforms existing methods and alternative\narchitectures by reaching the least estimation error on average (0.68, less\nthan one driver) and the highest identification accuracy (by at least 3%\nimprovement) compared with traditional supervised learning methods.","url_abs":"http://arxiv.org/abs/1701.01272v1","url_pdf":"http://arxiv.org/pdf/1701.01272v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"autoencoder-regularized-network-for-driving","repo_url":"https://github.com/sobhan-moosavi/arnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"driver-identification","task_name":"Driver Identification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}