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Probabilistic Motion Planning for Automated Vehicles

Naumann, Maximilian

Abstract (englisch):
This thesis targets the problem of motion planning for automated vehicles. As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered.

Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered.
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Volltext §
DOI: 10.5445/IR/1000123725
Veröffentlicht am 28.09.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Hochschulschrift
Publikationsdatum 28.09.2020
Sprache Englisch
Identifikator KITopen-ID: 1000123725
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XIII, 161 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Maschinenbau (MACH)
Institut Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Prüfungsdatum 27.08.2020
Referent/Betreuer Prof. C. Stiller
Schlagwörter Motion Planning, Planning under Uncertainty, Decision Making under Uncertainty, Safe Motion Planning, Probabilistic Motion Planning, Trajectory Planning, Automated Vehicles, Automated Driving, Self-Driving Vehicles
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