Time-varying system identification via maximum a posteriori estimation and its application to driver steering models

Te-Sheng Hsiao*

*此作品的通信作者

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Modern automotive technologies try to predict the driver's intention in order to control the vehicle effectively. However mathematical models describing the driver's steering behavior with sufficient accuracy are not available. The difficulties arise from the time-varying properties of the driver's behavior under rapidly changing traffic conditions. In this paper, a time-varying system identification method using maximum a posteriori estimation is proposed. An efficient iterative procedure is presented for maximizing the posterior probability of the parameters conditioning on observed data. Then it is applied to the experimental driving data, and the driver's time-varying steering models are identified and analyzed. The results indicate that the time-varying model reduces the output estimation errors significantly. Moreover, changes of driving strategies are observed from the identified models after drivers drive for a period of time.

原文American English
主出版物標題2008 American Control Conference, ACC
發行者IEEE
頁面684-689
頁數6
ISBN(列印)9781424420797
DOIs
出版狀態Published - 11 6月 2008
事件2008 American Control Conference, ACC - Seattle, WA, United States
持續時間: 11 6月 200813 6月 2008

出版系列

名字Proceedings of the American Control Conference
ISSN(列印)0743-1619

Conference

Conference2008 American Control Conference, ACC
國家/地區United States
城市Seattle, WA
期間11/06/0813/06/08

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