Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction
This work addresses the need for explainable and interpretable predictions in autonomous driving to improve road safety, representing an incremental advancement in the field.
The paper tackles pedestrian crossing prediction for autonomous driving by developing a neuro-symbolic method combining deep learning and fuzzy logic, resulting in an explainable predictor tested on PIE and JAAD datasets with guidelines for dataset and feature selection.
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.