Developing accurate models for traffic trajectory predictions is crucial for
achieving fully autonomous driving. Various deep neural network models have
been employed to address this challenge, but their black-box nature hinders
transparency and debugging capabilities in a deployed system. Glass-box models
offer a solution by providing full interpretability through methods like
\ac{GAM}. In this study, we evaluate an efficient additive model called
\ac{EBM} for traffic prediction on three popular mixed traffic datasets:
\ac{SDD}, \ac{InD}, and Argoverse. Our results show that the \ac{EBM} models
perform competitively in predicting pedestrian destinations within \ac{SDD} and
\ac{InD} while providing modest predictions for vehicle-dominant Argoverse
dataset. Additionally, our transparent trained models allow us to analyse
feature importance and interactions, as well as provide qualitative examples of
predictions explanation. The full training code will be made public upon
publication.

通过评估一种高效的可加性模型，本研究在三个热门混合交通数据集上对交通预测进行了评估，结果显示该模型在预测行人目的地方面表现竞争力，对以车辆为主的数据集则提供较为适度的预测，而透明的训练模型使我们能够分析特征的重要性和相互作用，并提供预测解释的质量性示例。

高效可解释的交通目的地预测：解释性提升机方法

Efficient and Interpretable Traffic Destination Prediction using  Explainable Boosting Machines

Compared to "black-box" models, like random forests and deep neural networks,
explainable boosting machines (EBMs) are considered "glass-box" models that can
be competitively accurate while also maintaining a higher degree of
transparency and explainability. However, EBMs become readily less transparent
and harder to interpret in high-dimensional settings with many predictor
variables; they also become more difficult to use in production due to
increases in scoring time. We propose a simple solution based on the least
absolute shrinkage and selection operator (LASSO) that can help introduce
sparsity by reweighting the individual model terms and removing the less
relevant ones, thereby allowing these models to maintain their transparency and
relatively fast scoring times in higher-dimensional settings. In short,
post-processing a fitted EBM with many (i.e., possibly hundreds or thousands)
of terms using the LASSO can help reduce the model's complexity and drastically
improve scoring time. We illustrate the basic idea using two real-world
examples with code.

使用最小绝对收缩与选择算子（LASSO）的简单解决方案可以通过重新加权各个模型项并删除不相关的项来引入稀疏性，从而使易解释的增强机模型在高维度环境中保持透明度和相对较快的评分时间。