  Motion planning at urban intersections that accounts for the situation
context, handles occlusions, and deals with measurement and prediction
uncertainty is a major challenge on the way to urban automated driving. In this
work, we address this challenge with a sampling-based optimization approach.
For this, we formulate an optimal control problem that optimizes for low risk
and high passenger comfort. The risk is calculated on the basis of the
perception information and the respective uncertainty using a risk model. The
risk model combines set-based methods and probabilistic approaches. Thus, the
approach provides safety guarantees in a probabilistic sense, while for a
vanishing risk, the formal safety guarantees of the set-based methods are
inherited. By exploring all available behavior options, our approach solves
decision making and longitudinal trajectory planning in one step. The available
behavior options are provided by a formal representation of the situation
context, which is also used to reduce calculation efforts. Occlusions are
resolved using the external perception of infrastructure-mounted sensors. Yet,
instead of merging external and ego perception with track-to-track fusion, the
information is used in parallel. The motion planning scheme is validated
through real-world experiments.
