Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and generalizability. This paper proposes the Goal-based Neural Variational Agent (GNeVA), an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians. We identify a causal structure among maps and agents' histories and derive a variational posterior to enhance generalizability. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable and can achieve comparable performance to state-of-the-art results.

翻译：估计周围人类驾驶车辆的潜在行为对于混合交通流中自动驾驶汽车的安全性至关重要。现有先进方法通过深度神经网络实现了精准预测。然而，这些端到端模型通常是缺乏可解释性和泛化性的黑箱。本文提出基于目标神经变分智能体（GNeVA），这是一种具有可解释性的运动预测生成模型，能对分布外场景展现出稳健的泛化能力。在可解释性方面，该模型通过变分高斯混合模型估计长期目的地的空间分布，实现目标驱动型的运动预测。我们识别出地图与智能体历史信息之间的因果结构，并推导出用于增强泛化能力的变分后验分布。在运动预测数据集上的实验表明，该拟合模型兼具可解释性与泛化性，且能达到与现有先进方法相当的性能。