  Autonomous robots and vehicles are expected to soon become an integral part
of our environment. Unsatisfactory issues regarding interaction with existing
road users, performance in mixed-traffic areas and lack of interpretable
behavior remain key obstacles. To address these, we present a physics-based
neural network, based on a hybrid approach combining a social force model
extended by group force (SFMG) with Multi-Layer Perceptron (MLP) to predict
pedestrian trajectories considering its interaction with static obstacles,
other pedestrians and pedestrian groups. We quantitatively and qualitatively
evaluate the model with respect to realistic prediction, prediction performance
and prediction "interpretability". Initial results suggest, the model even when
solely trained on a synthetic dataset, can predict realistic and interpretable
trajectories with better than state-of-the-art accuracy.
