  We consider the problem of predicting the future path of a pedestrian using
its motion history and the motion history of the surrounding pedestrians,
called social information. Since the seminal paper on Social-LSTM,
deep-learning has become the main tool used to model the impact of social
interactions on a pedestrian's motion. The demonstration that these models can
learn social interactions relies on an ablative study of these models. The
models are compared with and without their social interactions module on two
standard metrics, the Average Displacement Error and Final Displacement Error.
Yet, these complex models were recently outperformed by a simple
constant-velocity approach. This questions if they actually allow to model
social interactions as well as the validity of the proof. In this paper, we
focus on the deep-learning models with a soft-attention mechanism for social
interaction modeling and study whether they use social information at
prediction time. We conduct two experiments across four state-of-the-art
approaches on the ETH and UCY datasets, which were also used in previous work.
First, the models are trained by replacing the social information with random
noise and compared to model trained with actual social information. Second, we
use a gating mechanism along with a $L_0$ penalty, allowing models to shut down
their inner components. The models consistently learn to prune their
soft-attention mechanism. For both experiments, neither the course of the
convergence nor the prediction performance were altered. This demonstrates that
the soft-attention mechanism and therefore the social information are ignored
by the models.
