Becker, StefanStefanBeckerHug, RonnyRonnyHugHübner, WolfgangWolfgangHübnerArens, MichaelMichaelArens2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/40461310.1007/978-3-030-20205-7_32The 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.entrajectory forecastingpath predictionIMM filtermultiple model filter004670conference paper