Motion Planning for Autonomous Vehicles in Partially Observable Environments
dc.contributor.author | Taş, Ömer Şahin | |
dc.date.accessioned | 2023-10-31T13:48:53Z | |
dc.date.available | 2023-10-31T13:48:53Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/77094 | |
dc.description.abstract | This 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.language | English | en_US |
dc.relation.ispartofseries | Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie | en_US |
dc.subject.other | Robotics; Planning under Uncertainty; Decision Making; Information Gathering; Motion Planning; Robotik; Automatisiertes Fahren; Planung unter Unsicherheiten; Entscheidungsfindung; Bewegungsplanung | en_US |
dc.title | Motion Planning for Autonomous Vehicles in Partially Observable Environments | en_US |
dc.type | book | |
oapen.identifier.doi | 10.5445/KSP/1000158509 | en_US |
oapen.relation.isPublishedBy | 44e29711-8d53-496b-85cc-3d10c9469be9 | en_US |
oapen.series.number | 48 | en_US |
oapen.pages | 222 | en_US |