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 (≈40 seq. per second) and size (8×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.
@article{arxiv.2105.08647,
title = {IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture},
author = {J. Lorenzo and I. Parra and M. A. Sotelo},
journal= {arXiv preprint arXiv:2105.08647},
year = {2021}
}