This thesis describes a motion planning system for automated vehicles. The planning method is universally applicable in on-road scenarios and does not depend on a high-level maneuver selection automaton for driving strategy guidance. The majority of additional driving modes will therefore be processed in one particular processing unit for future high and full automation systems. The thesis pursues three research questions that have given rise to all the solutions presented. What are the capabilities of a universal method? How can perception quality benefit from motion planning? And how can robustness against motion uncertainty be ensured?

A planning framework using graphics processing units (GPUs) for task parallelization is presented. 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. For further assessment, planning benchmark criteria have been derived and applied.

Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximize scene knowledge through smart vehicle positioning is evaluated. It can be shown through experimental results that areas marked as relevant are more often covered by sensors by minor adjustments of the initial optimal planning solution.

A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. This achievement is a gain in the trajectory’s temporal validity.