Motion Planning for Autonomous Vehicles in Partially Observable Environments
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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Main Author: | |
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
KIT Scientific Publishing
2023
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Series: | Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
48 |
Subjects: | |
Online Access: | OAPEN Library: download the publication OAPEN Library: description of 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. |
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Physical Description: | 1 electronic resource (222 p.) |
ISBN: | KSP/1000158509 |
Access: | Open Access |