  Predicting human trajectories is a challenging task due to the complexity of
pedestrian behavior, which is influenced by external factors such as the
scene's topology and interactions with other pedestrians. A special challenge
arises from the dependence of the behaviour on the density of the scene. In the
literature, deep learning algorithms show the best performance in predicting
pedestrian trajectories, but so far just for situations with low densities. In
this study, we aim to investigate the suitability of these algorithms for
high-density scenarios by evaluating them on different error metrics and
comparing their accuracy to that of knowledge-based models that have been used
since long time in the literature. The findings indicate that deep learning
algorithms provide improved trajectory prediction accuracy in the distance
metrics for all tested densities. Nevertheless, we observe a significant number
of collisions in the predictions, especially in high-density scenarios. This
issue arises partly due to the absence of a collision avoidance mechanism
within the algorithms and partly because the distance-based collision metric is
inadequate for dense situations. To address these limitations, we propose the
introduction of a novel continuous collision metric based on pedestrians'
time-to-collision. Subsequently, we outline how this metric can be utilized to
enhance the training of the algorithms.
