Planning Universal On-Road Driving Strategies for Automated Vehicles

Description

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. Front Matter ....Pages I-XV Introduction, motivation and structure of the thesis (Steffen Heinrich)....Pages 1-5 Preliminaries (Steffen Heinrich)....Pages 7-19 Related work (Steffen Heinrich)....Pages 21-32 A framework for universal driving strategy planning (Steffen Heinrich)....Pages 33-47 Sampling-based planning in phase space (Steffen Heinrich)....Pages 49-73 A universal approach for driving strategies (Steffen Heinrich)....Pages 75-101 Modeling ego motion uncertainty (Steffen Heinrich)....Pages 103-116 Summary, outlook and contributions (Steffen Heinrich)....Pages 117-120 Back Matter ....Pages 121-133