  The problem of varying dynamics of tracked objects, such as pedestrians, is
traditionally tackled with approaches like the Interacting Multiple Model (IMM)
filter using a Bayesian formulation. By following the current trend towards
using deep neural networks, in this paper an RNN-based IMM filter surrogate is
presented. Similar to an IMM filter solution, the presented RNN-based model
assigns a probability value to a performed dynamic and, based on them, puts out
a multi-modal distribution over future pedestrian trajectories. The evaluation
is done on synthetic data, reflecting prototypical pedestrian maneuvers.
