The ability to predict the trajectories of pedestrians
and cars is an important task for tasks such as autonomous
driving and navigation for robots. Many current state-of-art
methods are trained using contrastive methods that require
human labelled data about the pedestrians, for instance their
current action e.g., walking or standing. Human labelled
data is both expensive and time-consuming to produce. In
this study, I will present a method using a non-contrastive
method, which produces competitive results without the need
for human labelled data. Instead of comparing the action
labels of pedestrians, the model uses different augmentations
of the data to learn similar representations. Experiments
for the proposed method are conducted on both first-person
view (FPV) datasets and bird’s-eye view (BEV) datasets. This
method provides competitive results to existing state-of-the-art
methods, including methods that make use of human labeled
annotations. The results of this paper should provide further
research a base to work from and expand further upon this
topic.