  Predicting the movement trajectories of multiple classes of road users in
real-world scenarios is a challenging task due to the diverse trajectory
patterns. While recent works of pedestrian trajectory prediction successfully
modelled the influence of surrounding neighbours based on the relative
distances, they are ineffective on multi-class trajectory prediction. This is
because they ignore the impact of the implicit correlations between different
types of road users on the trajectory to be predicted - for example, a nearby
pedestrian has a different level of influence from a nearby car. In this paper,
we propose to introduce class information into a graph convolutional neural
network to better predict the trajectory of an individual. We embed the class
labels of the surrounding objects into the label adjacency matrix (LAM), which
is combined with the velocity-based adjacency matrix (VAM) comprised of the
objects' velocity, thereby generating a semantics-guided graph adjacency (SAM).
SAM effectively models semantic information with trainable parameters to
automatically learn the embedded label features that will contribute to the
fixed velocity-based trajectory. Such information of spatial and temporal
dependencies is passed to a graph convolutional and temporal convolutional
network to estimate the predicted trajectory distributions. We further propose
new metrics, known as Average2 Displacement Error (aADE) and Average Final
Displacement Error (aFDE), that assess network accuracy more accurately. We
call our framework Semantics-STGCNN. It consistently shows superior performance
to the state-of-the-arts in existing and the newly proposed metrics.
