This work presents a novel rule-based interaction-aware
multi-modal prediction method for urban traffic scenarios.
The method takes into account the most common classes of
traffic participants and handles all relevant types of
motion behaviors. The potential trajectories of the traffic
participants are rolled out resulting in multi-modal
probability distributions for the states of all agents for
each prediction time step. The analysis of collision risks
between these trajectories is the basis for the
interaction-awareness. The prediction is fully
interaction-aware by considering also the interactions
between the obstacles. The system is able to predict
complex urban scenarios with numerous different agents in
real-time. The approach is evaluated using real-world
scenarios and in a simulated environments.