Overview
- GPU enabled method for trajectory optimization
Part of the book series: AutoUni – Schriftenreihe (AUS, volume 119)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
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.
Bibliographic Information
Book Title: Planning Universal On-Road Driving Strategies for Automated Vehicles
Authors: Steffen Heinrich
Series Title: AutoUni – Schriftenreihe
DOI: https://doi.org/10.1007/978-3-658-21954-3
Publisher: Springer Wiesbaden
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2018
Softcover ISBN: 978-3-658-21953-6Published: 27 April 2018
eBook ISBN: 978-3-658-21954-3Published: 19 April 2018
Series ISSN: 1867-3635
Series E-ISSN: 2512-1154
Edition Number: 1
Number of Pages: XV, 133
Number of Illustrations: 34 b/w illustrations, 25 illustrations in colour
Topics: Robotics and Automation, Artificial Intelligence, Numeric Computing