  Understanding and predicting the intention of pedestrians is essential to
enable autonomous vehicles and mobile robots to navigate crowds. This problem
becomes increasingly complex when we consider the uncertainty and multimodality
of pedestrian motion, as well as the implicit interactions between members of a
crowd, including any response to a vehicle. Our approach, Probabilistic Crowd
GAN, extends recent work in trajectory prediction, combining Recurrent Neural
Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic
multimodal predictions, from which likely modal paths are found and used for
adversarial training. We also propose the use of Graph Vehicle-Pedestrian
Attention Network (GVAT), which models social interactions and allows input of
a shared vehicle feature, showing that inclusion of this module leads to
improved trajectory prediction both with and without the presence of a vehicle.
Through evaluation on various datasets, we demonstrate improvements on the
existing state of the art methods for trajectory prediction and illustrate how
the true multimodal and uncertain nature of crowd interactions can be directly
modelled.
