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Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

Hubmann, Constantin

Abstract (englisch):
This thesis presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. Even the perception of objects is uncertain due to sensor noise or possible occlusions. When driving in such environments, the autonomous car must predict the behavior of the other drivers and plan safe, comfortable and legal trajectories. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car.
Current planning algorithms for automated driving split the problem into different subproblems, ranging from discrete, high-level decision making to prediction and continuous trajectory planning. This separation of one problem into several subproblems, combined with rule-based decision making, leads to sub-optimal behavior.
This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. ... mehr

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Volltext §
DOI: 10.5445/IR/1000120841
Veröffentlicht am 27.07.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Hochschulschrift
Publikationsdatum 27.07.2020
Sprache Englisch
Identifikator KITopen-ID: 1000120841
Verlag Karlsruhe
Umfang XIII, 142 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Maschinenbau (MACH)
Institut Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Prüfungsdatum 08.11.2019
Referent/Betreuer Prof. C. Stiller
Schlagwörter Behavior Planning for Autonomous Driving, Planning under Uncertainty, Belief State Planning, Decision Making under Uncertainty
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