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Dynamic Decision-making in Continuous Partially Observable Domains: A Novel Method and its Application for Autonomous Driving

Brechtel, Sebastian

Abstract (englisch):
Decision-making is a crucial challenge on the way to fully autonomous systems. In real world tasks, assessing the consequences of decisions is aggravated by two factors: uncertainty and the continuous nature of the environment. In this work, we develop a general method for solving continuous partially observable Markov decision processes (POMDPs) that combines learning and planning. We apply it to autonomous driving in urban scenarios with hidden objects and cooperative driver interactions.

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Volltext §
DOI: 10.5445/IR/1000054518
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2015
Sprache Englisch
Identifikator urn:nbn:de:swb:90-545182
KITopen-ID: 1000054518
Verlag KIT, Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdaten 06.07.2015
Referent/Betreuer Prof. R. Dillmann
Schlagworte planning, learning, pomdp, autonomous driving, probabilistic
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