Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than simple rule-based models. To address this limitation, we propose a novel framework, Adaptive Prediction Ensemble (APE), which integrates deep learning and rule-based prediction experts. A learned routing function, trained concurrently with the deep learning model, dynamically selects the most reliable prediction based on the input scenario. Our experiments on large-scale datasets, including Waymo Open Motion Dataset (WOMD) and Argoverse, demonstrate improvement in zero-shot generalization across datasets. We show that our method outperforms individual prediction models and other variants, particularly in long-horizon prediction and scenarios with a high proportion of OOD data. This work highlights the potential of hybrid approaches for robust and generalizable motion prediction in autonomous driving.

翻译：基于深度学习的自动驾驶轨迹预测模型在泛化至分布外（OOD）场景时常常表现不佳，有时甚至不如简单的基于规则的模型。为应对这一局限，我们提出了一种新颖的框架——自适应预测集成（APE），该框架融合了深度学习与基于规则的预测专家模型。一个与深度学习模型同步训练的学习型路由函数，能够根据输入场景动态选择最可靠的预测。我们在包括Waymo开放运动数据集（WOMD）和Argoverse在内的大规模数据集上进行的实验表明，该方法提升了跨数据集的零样本泛化能力。我们证明，本方法优于单一的预测模型及其他变体，尤其在长时程预测和OOD数据占比较高的场景中表现突出。这项工作凸显了混合方法在实现自动驾驶中鲁棒且可泛化的运动预测方面的潜力。