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In the future, pedestrians and fully automated vehicles have to operate in an environment they share. To minimize the risk for pedestrians, it is very important to predict precisely their future movement. One important information source is the intention of the pedestrian. For the integration of the intention information, a Multi-Hypotheses filter is used, where different hypotheses for the intention of the pedestrian are considered. An intention change detector based on the Multi-Hypotheses filter utilizing an entropy-based confidence score is developed. With this contribution, critical real-world situations like a pedestrian crossing the street instead of following the sidewalk are tackled. The evaluation of the intention change detector is performed in simulation and for real-world data. Firstly, the proposed approach is evaluated using simulated trajectory data, where trajectories with intention changes are generated by a self …