  Deep neural networks (DNNs) have become remarkably successful in data
prediction, and have even been used to predict future actions based on limited
input. This raises the question: do these systems actually "understand" the
event similar to humans? Here, we address this issue using videos taken from an
accident situation in a driving simulation. In this situation, drivers had to
choose between crashing into a suddenly-appeared obstacle or steering their car
off a previously indicated cliff. We compared how well humans and a DNN
predicted this decision as a function of time before the event. The DNN
outperformed humans for early time-points, but had an equal performance for
later time-points. Interestingly, spatio-temporal image manipulations and
Grad-CAM visualizations uncovered some expected behavior, but also highlighted
potential differences in temporal processing for the DNN.
