  Forecasting the trajectory of pedestrians in shared urban traffic
environments is still considered one of the challenging problems facing the
development of autonomous vehicles (AVs). In the literature, this problem is
often tackled using recurrent neural networks (RNNs). Despite the powerful
capabilities of RNNs in capturing the temporal dependency in the pedestrians'
motion trajectories, they were argued to be challenged when dealing with longer
sequential data. Thus, in this work, we are introducing a framework based on
the transformer networks that were shown recently to be more efficient and
outperformed RNNs in many sequential-based tasks. We relied on a fusion of the
past positional information, agent interactions information and scene physical
semantics information as an input to our framework in order to provide a robust
trajectory prediction of pedestrians. We have evaluated our framework on two
real-life datasets of pedestrians in shared urban traffic environments and it
has outperformed the compared baseline approaches in both short-term and
long-term prediction horizons.
