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Motion Planning for Autonomous Vehicles in Partially Observable Environments

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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...

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Bibliographic Details
Main Author: Taş, Ömer Şahin (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2023
Series:Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
Subjects:
Building construction & materials
Robotics; Planning under Uncertainty; Decision Making; Information Gathering; Motion Planning; Robotik; Automatisiertes Fahren; Planung unter Unsicherheiten; Entscheidungsfindung; Bewegungsplanung
Online Access:DOAB: download the publication
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Summary: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.
Physical Description:1 electronic resource (222 p.)
ISBN:KSP/1000158509
Access:Open Access

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