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

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors