  Anticipating human actions in front of autonomous vehicles is a challenging
task. Several papers have recently proposed model architectures to address this
problem by combining multiple input features to predict pedestrian crossing
actions. This paper focuses specifically on using images of the pedestrian's
context as an input feature. We present several spatio-temporal model
architectures that utilize standard CNN and Transformer modules to serve as a
backbone for pedestrian anticipation. However, the objective of this paper is
not to surpass state-of-the-art benchmarks but rather to analyze the positive
and negative predictions of these models. Therefore, we provide insights on the
explainability of vision-based Transformer models in the context of pedestrian
action prediction. We will highlight cases where the model can achieve correct
quantitative results but falls short in providing human-like explanations
qualitatively, emphasizing the importance of investing in explainability for
pedestrian action anticipation problems.
