View source for Publications:Model-based estimation of driver intentions using particle filtering

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{{PublicationSetupTemplate|Author=Kristoffer Lidström, Tony Larsson
|PID=239521
|Name=Lidström, Kristoffer [krilid] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Centrum för forskning om inbyggda system (CERES) [6702]);Larsson, Tony [tola] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Centrum för forskning om inbyggda system (CERES) [6702])
|Title=Model-based estimation of driver intentions using particle filtering
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
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|HostPublication=11th International IEEE Conference on Intelligent Transportation Systems, 2008. ITSC 2008
|Conference=11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, October 12-15, 2008
|StartPage=1177
|EndPage=1182
|Year=2008
|Edition=
|Pages=
|City=Piscataway, NJ.
|Publisher=IEEE Press
|Series=
|SeriesISSN=
|ISBN=978-1-4244-2111-4
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|DOI=http://dx.doi.org/10.1109/ITSC.2008.4732623
|ISI=000262929300197
|PMID=
|ScopusId=2-s2.0-60749090048
|NBN=urn:nbn:se:hh:diva-2303
|LocalId=2082/2705
|ArchiveNumber=
|Keywords=Vehicle alert systems;Prediction method
|Categories=Teknik och teknologier (2)
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|Notes=<p>©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p>
|Abstract=<p>Proactive vehicle alert systems that warn the driver about dangerous situations must be able to reason about, and predict, likely future states of the traffic environment. Our prediction method is based on a combination of a fuzzy logic model for intersection turning behavior and Gipps model for car following behavior. The stochastic models are used together with a particle filter to recursively approximate the state probability distribution as measurements are received over time. Estimates of the unobservable part of the state are used to predict path choice and thus driver intentions. The approach is evaluated on trajectory data gathered from video footage of an intersection, however it is also relevant for trajectories acquired through vehicle-to-vehicle communication.</p>
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|CreatedDate=2009-02-16
|PublicationDate=2009-02-16
|LastUpdated=2012-10-02
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239521}}
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