  Motion planning is an essential part of autonomous mobile platforms. A good
pipeline should be modular enough to handle different vehicles, environments,
and perception modules. The planning process has to cope with all the different
modalities and has to have a modular and flexible design. But most importantly,
it has to be safe and robust. In this paper, we want to present our motion
planning pipeline with particle swarm optimization (PSO) at its core. This
solution is independent of the vehicle type and has a clear and
simple-to-implement interface for perception modules. Moreover, the approach
stands out for being easily adaptable to new scenarios. Parallel calculation
allows for fast planning cycles. Following the principles of PSO, the
trajectory planer first generates a swarm of initial trajectories that are
optimized afterward. We present the underlying control space and inner
workings. Finally, the application to real-world automated driving is shown in
the evaluation with a deeper look at the modeling of the cost function. The
approach is used in our automated shuttles that have already driven more than
3.500 km safely and entirely autonomously in sub-urban everyday traffic.
