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.