  Motion prediction for intelligent vehicles typically focuses on estimating
the most probable future evolutions of a traffic scenario. Estimating the gap
acceptance, i.e., whether a vehicle merges or crosses before another vehicle
with the right of way, is often handled implicitly in the prediction. However,
an infrastructure-based maneuver planning can assign artificial priorities
between cooperative vehicles, so it needs to evaluate many more potential
scenarios. Additionally, the prediction horizon has to be long enough to assess
the impact of a maneuver. We, therefore, present a novel long-term prediction
approach handling the gap acceptance estimation and the velocity prediction in
two separate stages. Thereby, the behavior of regular vehicles as well as
priority assignments of cooperative vehicles can be considered. We train both
stages on real-world traffic observations to achieve realistic prediction
results. Our method has a competitive accuracy and is fast enough to predict a
multitude of scenarios in a short time, making it suitable to be used in a
maneuver planning framework.
