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 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 that take ego-motion uncertainty into account.

Contents:

• A framework for universal driving strategy planning
• Sampling-based planning in phase space
• A universal approach for driving strategies
• Modeling ego motion uncertainty

Target Groups:

• Scientists and students in the field of robotics, computer science, mechanical engineering
• Engineers in the field of vehicle automation, intelligent systems, and robotics

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.