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AuthorBerndt, Holgerdc.contributor.author
Date of accession2016-03-15T10:40:11Zdc.date.accessioned
Available in OPARU since2016-03-15T10:40:11Zdc.date.available
Year of creation2015dc.date.created
AbstractThe intention of drivers to start discrete manoeuvres (like a lane change or a turn manoeuvre) is identified as a major factor determining the dynamic scene within the next couple of seconds. Hidden Markov Models are successfully applied for an early detection of beginning driving manoeuvres. The models use signals of the driving dynamics of the vehicle, digital maps, and a camera-based lane detection as features. By limiting the Markov chains to linear models, it is possible to train complete manoeuvres, and afterwards extract meaningful submodels. The submodels are chosen in a way to fit on manoeuvre starts, in order to ensure early detection. Manoeuvre recognition can be purely based on the probability of the observation sequence which allows for a computationally efficient classification. Decoding steps are not necessary. The linear structure of the Hidden Markov models also enables the determination of "typical" paths of an observation sequence through the Markov chain. The result is not only a yet more computationally efficient classification possibility, but also a measure that is more robust in the presence of differently scaled variances of observations and difficulties introduced by the geometrically distributed state transitions inherent to Markov Models. The classification performance is evaluated using a manually labelled reference database of field-recorded driving data. Based on the probabilistic information about upcoming manoeuvres, a trajectory prediction can be performed. In order to assess the risk of an evolving traffic situation, collision probabilities are evaluated. As determining collision probabilities is a computationally intense operation, efficient approximation methods are proposed and evaluated.dc.description.abstract
Languagededc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY-NC-ND 3.0 Deutschlanddc.rights
Link to license texthttp://creativecommons.org/licenses/by-nc-nd/3.0/de/dc.rights.uri
KeywordFahrstreifenerkennungdc.subject
KeywordHuman behaviour predictiondc.subject
KeywordIntention recognitiondc.subject
KeywordKollisionswahrscheinlichkeitdc.subject
KeywordManövererkennungdc.subject
Dewey Decimal GroupDDC 600 / Technology (Applied sciences)dc.subject.ddc
LCSHDriver assistance systemsdc.subject.lcsh
LCSHMarkov processesdc.subject.lcsh
TitleFahrerabsichtserkennung und Gefährlichkeitsabschätzung für vorausschauende Fahrerassistenzsystemedc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3259dc.identifier.doi
PPN847969819dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-99334dc.identifier.urn
GNDDigitale Kartedc.subject.gnd
GNDFahrerassistenzsystemdc.subject.gnd
GNDFahrerverhaltendc.subject.gnd
GNDFehlererkennungdc.subject.gnd
GNDHidden-Markov-Modelldc.subject.gnd
GNDKraftfahrzeugdc.subject.gnd
GNDMensch-Maschine-Systemdc.subject.gnd
GNDRisikoanalysedc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2016-02-12T13:56:37Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionW: W-H 14.375uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS ID9933uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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