{"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/characterizing-driving-styles-with-deep","title":"Characterizing Driving Styles with Deep Learning","arxiv_id":"1607.03611","date":"2016-07-13","proceeding":null,"authors":["Weishan Dong","Jian Li","Renjie Yao","Changsheng Li","Ting Yuan","Lanjun Wang"],"abstract":"Characterizing driving styles of human drivers using vehicle sensor data,\ne.g., GPS, is an interesting research problem and an important real-world\nrequirement from automotive industries. A good representation of driving\nfeatures can be highly valuable for autonomous driving, auto insurance, and\nmany other application scenarios. However, traditional methods mainly rely on\nhandcrafted features, which limit machine learning algorithms to achieve a\nbetter performance. In this paper, we propose a novel deep learning solution to\nthis problem, which could be the first attempt of extending deep learning to\ndriving behavior analysis based on GPS data. The proposed approach can\neffectively extract high level and interpretable features describing complex\ndriving patterns. It also requires significantly less human experience and\nwork. The power of the learned driving style representations are validated\nthrough the driver identification problem using a large real dataset.","url_abs":"http://arxiv.org/abs/1607.03611v2","url_pdf":"http://arxiv.org/pdf/1607.03611v2.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":"characterizing-driving-styles-with-deep","repo_url":"https://github.com/sobhan-moosavi/DCRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"characterizing-driving-styles-with-deep","repo_url":"https://github.com/sobhan-moosavi/characterizingdrivingstyleswithdeeplearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"driver-identification","task_name":"Driver Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}