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

Te-Sheng Hsiao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2008 American Control Conference, ACC
Pages684-689
Number of pages6
DOIs
StatePublished - 30 Sep 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: 11 Jun 200813 Jun 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2008 American Control Conference, ACC
CountryUnited States
CitySeattle, WA
Period11/06/0813/06/08

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