  Predicting the trajectory of an ego vehicle is a critical component of
autonomous driving systems. Current state-of-the-art methods typically rely on
Deep Neural Networks (DNNs) and sequential models to process front-view images
for future trajectory prediction. However, these approaches often struggle with
perspective issues affecting object features in the scene. To address this, we
advocate for the use of Bird's Eye View (BEV) perspectives, which offer unique
advantages in capturing spatial relationships and object homogeneity. In our
work, we leverage Graph Neural Networks (GNNs) and positional encoding to
represent objects in a BEV, achieving competitive performance compared to
traditional DNN-based methods. While the BEV-based approach loses some detailed
information inherent to front-view images, we balance this by enriching the BEV
data by representing it as a graph where relationships between the objects in a
scene are captured effectively.
