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. As classical methods subdivide the motion planning into decision making and trajectory planning, they either come with a narrowed solution set or finding a feasible trajectory is not guaranteed. In this work, motion planning is formulated as an optimal control problem (OCP) and holistically solved for exploring all available decision options. The OCP is parametrized and simplified according to the situation context extracted from map and perception information. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track (T2T) fusion, the information is used in parallel. The uncertainties are handled by a risk model that bridges the gap between set-based methods and probabilistic approaches. Particularly, for vanishing risk, the formal guarantees of set-based methods are inherited, while otherwise, the guarantees are softened to guarantees in a probabilistic sense. This video shows a short presentation of the motion planning approach as well as results from the real-world experiments.

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