  Understanding pedestrian crossing behavior is an essential goal in
intelligent vehicle development, leading to an improvement in their security
and traffic flow. In this paper, we developed a method called IntFormer. It is
based on transformer architecture and a novel convolutional video
classification model called RubiksNet. Following the evaluation procedure in a
recent benchmark, we show that our model reaches state-of-the-art results with
good performance ($\approx 40$ seq. per second) and size ($8\times $smaller
than the best performing model), making it suitable for real-time usage. We
also explore each of the input features, finding that ego-vehicle speed is the
most important variable, possibly due to the similarity in crossing cases in
PIE dataset.
