Experimental Insights Towards Explainable and Interpretable Pedestrian
  Crossing Prediction
Angie Nataly Melo Carlota Salinas Miguel Angel Sotelo
In the context of autonomous driving, pedestrian crossing prediction is a key
component for improving road safety. Presently, the focus of these predictions
extends beyond achieving trustworthy results; it is shifting towards the
explainability and interpretability of these predictions. This research
introduces a novel neuro-symbolic approach that combines deep learning and
fuzzy logic for an explainable and interpretable pedestrian crossing
prediction. We have developed an explainable predictor (ExPedCross), which
utilizes a set of explainable features and employs a fuzzy inference system to
predict whether the pedestrian will cross or not. Our approach was evaluated on
both the PIE and JAAD datasets. The results offer experimental insights into
achieving explainability and interpretability in the pedestrian crossing
prediction task. Furthermore, the testing results yield a set of guidelines and
recommendations regarding the process of dataset selection, feature selection,
and explainability.