Details

Planning Universal On-Road Driving Strategies for Automated Vehicles

AutoUni - Schriftenreihe, Band 119

von: Steffen Heinrich
58,84 €
Verlag:	Springer
Format:	PDF
Veröffentl.:	19.04.2018
ISBN/EAN:	9783658219543
Sprache:	englisch

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Beschreibungen

Titelbeschreibung

<p>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.</p><p><b>About the Author</b></p><p> </p><p><b>Steffen Heinrich</b> 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.</p>

Inhaltsverzeichnis

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

Autorenportrait

<p><b>Steffen Heinrich</b> 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.</p>

Back cover copy

<div>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.</div><div><br></div><div><b>Contents</b></div><div><ul><li>AFramework for Universal Driving Strategy Planning<br></li><li>Sampling-Based Planning in Phase Space<br></li><li>A Universal Approach for Driving Strategies<br></li><li>Modeling Ego Motion Uncertainty<br></li></ul></div><div><b>Target Groups</b></div><div><ul><li>Scientists and students in the field of robotics, computer science, mechanical engineering<br></li><li>Engineers in the field of vehicle automation, intelligent systems and robotics<br></li></ul></div><div><b>About the Author</b></div><div><b>Steffen Heinrich</b> 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.</div><div><br></div>