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.

介绍了一种结合深度学习和模糊逻辑的新型神经符号方法，用于解释和解释行人过街预测，在 PIE 和 JAAD 数据集上评估，提供了在行人过街预测任务中实现可解释性和可解释性的实验见解，以及有关数据集选择、特征选择和解释过程的一系列指南和建议。

解释和可解释的行人过街预测的实验见解

Experimental Insights Towards Explainable and Interpretable Pedestrian  Crossing Prediction

Pedestrian crossing prediction has been a topic of active research, resulting
in many new algorithmic solutions. While measuring the overall progress of
those solutions over time tends to be more and more established due to the new
publicly available benchmark and standardized evaluation procedures, knowing
how well existing predictors react to unseen data remains an unanswered
question. This evaluation is imperative as serviceable crossing behavior
predictors should be set to work in various scenarii without compromising
pedestrian safety due to misprediction. To this end, we conduct a study based
on direct cross-dataset evaluation. Our experiments show that current
state-of-the-art pedestrian behavior predictors generalize poorly in
cross-dataset evaluation scenarii, regardless of their robustness during a
direct training-test set evaluation setting. In the light of what we observe,
we argue that the future of pedestrian crossing prediction, e.g. reliable and
generalizable implementations, should not be about tailoring models, trained
with very little available data, and tested in a classical train-test scenario
with the will to infer anything about their behavior in real life. It should be
about evaluating models in a cross-dataset setting while considering their
uncertainty estimates under domain shift.

该研究论文基于直接的跨数据集评估，研究目前最先进的行人行为预测器在交叉数据集评估情境下的泛化能力较差，论文主张未来的行人交叉预测应该考虑到领域转移下的不确定性估计。