Under CopyrightBecker, StefanStefanBecker2022-03-1419.7.20192019https://publica.fraunhofer.de/handle/publica/40488910.24406/publica-fhg-404889The dynamics of objects, such as pedestrians, varies over time. Commonly this problem is tackled with traditional approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. Following the current trend towards using deep neural networks, in this paper an RNN-based alternative solution for pedestrian maneuver prediction is presented. Similar to an IMM filter solution, the presented model assigns a confidence value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The qualitative evaluation is done on synthetic data, reflecting prototypical pedestrian maneuvers.en004670conference paper