  Pedestrian crossing prediction is a crucial task for autonomous driving.
Numerous studies show that an early estimation of the pedestrian's intention
can decrease or even avoid a high percentage of accidents. In this paper,
different variations of a deep learning system are proposed to attempt to solve
this problem. The proposed models are composed of two parts: a CNN-based
feature extractor and an RNN module. All the models were trained and tested on
the JAAD dataset. The results obtained indicate that the choice of the features
extraction method, the inclusion of additional variables such as pedestrian
gaze direction and discrete orientation, and the chosen RNN type have a
significant impact on the final performance.
