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Beschreibung
Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account.
About the Author

Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account.
About the Author

Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
Über den Autor
Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
Zusammenfassung

GPU enabled method for trajectory optimization

Inhaltsverzeichnis

A Framework for Universal Driving Strategy Planning.- Sampling-Based Planning in Phase Space.- A Universal Approach for Driving Strategies.- Modeling Ego Motion Uncertainty.

Details
Erscheinungsjahr: 2018
Fachbereich: Fertigungstechnik
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xv
133 S.
34 s/w Illustr.
25 farbige Illustr.
133 p. 59 illus.
25 illus. in color.
ISBN-13: 9783658219536
ISBN-10: 365821953X
Sprache: Englisch
Herstellernummer: 978-3-658-21953-6
Einband: Kartoniert / Broschiert
Autor: Heinrich, Steffen
Hersteller: Springer
Springer Vieweg
Springer Fachmedien Wiesbaden GmbH
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 210 x 148 x 9 mm
Von/Mit: Steffen Heinrich
Erscheinungsdatum: 27.04.2018
Gewicht: 0,207 kg
Artikel-ID: 113410287

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