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.
