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。