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dc.contributor.authorWeiser, Andreas*
dc.date.accessioned2021-02-11T23:55:23Z
dc.date.available2021-02-11T23:55:23Z
dc.date.issued2019*
dc.date.submitted2019-07-30 20:02:01*
dc.identifier35320*
dc.identifier.issn16134214*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/57010
dc.description.abstractA concept for time-related forecasts of lane change maneuvers in highway scenarios is presented within the present work. Automated driving systems rely on understanding the driving environment to fulfill their driving task transparently and safely. This involves the perception of the driving environment as well as its interpretation to detect and predict driving maneuvers of road users.*
dc.languageGerman*
dc.relation.ispartofseriesSchriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie*
dc.subjectT1-995*
dc.subject.otherFahrstreifenwechsel*
dc.subject.otherAutomatisches Fahren*
dc.subject.otherMaschinelles Lernen*
dc.subject.otherdynamic Bayesian networks*
dc.subject.otherDynamische Bayes'sche Netzwerke*
dc.subject.otherlane change*
dc.subject.othermachine learning*
dc.subject.otherautomated driving*
dc.titleProbabilistische Vorhersage von Fahrstreifenwechseln für hochautomatisiertes Fahren auf Autobahnen*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000082533*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
virtual.oapen_relation_isPublishedBy.publisher_nameKIT Scientific Publishing
virtual.oapen_relation_isPublishedBy.publisher_websitehttp://www.ksp.kit.edu/
oapen.relation.isbn9783731507949*
oapen.pagesXI, 232 p.*
oapen.volume043*


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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-sa/4.0/