  Autonomous transportation systems such as road vehicles or vessels require
the consideration of the static and dynamic environment to dislocate without
collision. Anticipating the behavior of an agent in a given situation is
required to adequately react to it in time. Developing deep learning-based
models has become the dominant approach to motion prediction recently. The
social environment is often considered through a CNN-LSTM-based sub-module
processing a $\textit{social tensor}$ that includes information of the past
trajectory of surrounding agents. For the proposed transformer-based trajectory
prediction model, an alternative, computationally more efficient social tensor
definition and processing is suggested. It considers the interdependencies
between target and surrounding agents at each time step directly instead of
relying on information of last hidden LSTM states of individually processed
agents. A transformer-based sub-module, the Social Tensor Transformer, is
integrated into the overall prediction model. It is responsible for enriching
the target agent's dislocation features with social interaction information
obtained from the social tensor. For the awareness of spatial limitations,
dislocation features are defined in relation to the navigable area. This
replaces additional, computationally expensive map processing sub-modules. An
ablation study shows, that for longer prediction horizons, the deviation of the
predicted trajectory from the ground truth is lower compared to a spatially and
socially agnostic model. Even if the performance gain from a spatial-only to a
spatial and social context-sensitive model is small in terms of common error
measures, by visualizing the results it can be shown that the proposed model in
fact is able to predict reactions to surrounding agents and explicitely allows
an interpretable behavior.
