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dc.contributor.authorTaş, Ömer Şahin
dc.date.accessioned2023-10-31T13:48:53Z
dc.date.available2023-10-31T13:48:53Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/77094
dc.description.abstractThis work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesSchriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologieen_US
dc.subject.otherRobotics; Planning under Uncertainty; Decision Making; Information Gathering; Motion Planning; Robotik; Automatisiertes Fahren; Planung unter Unsicherheiten; Entscheidungsfindung; Bewegungsplanungen_US
dc.titleMotion Planning for Autonomous Vehicles in Partially Observable Environmentsen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000158509en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.series.number48en_US
oapen.pages222en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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