Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability to predict future trajectories for all moving objects in the surroundings and quantify prediction uncertainties. In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object. Our approach can distinguish Out-of-Distribution data while quantifying uncertainty and achieving competitive performance compared to state-of-the-art methods on the Argoverse 2 and INTERACTION datasets. Specifically, a 0.446 meters minimum Final Displacement Error, a 0.203 meters minimum Average Displacement Error, and a 5.35% Miss Rate are achieved on the INTERACTION test set. Extensive qualitative and quantitative analysis is also provided to evaluate the proposed model. Our open-source code is available at https://github.com/PurdueDigitalTwin/seneva.

翻译：安全性与鲁棒性是开发可信自动驾驶车辆的关键因素。解决这些因素的重要途径之一，是赋予车辆预测周围所有运动物体未来轨迹并量化预测不确定性的能力。本文提出序贯神经变分智能体（Sequential Neural Variational Agent, SeNeVA），这是一个生成式模型，用于描述单个运动物体未来轨迹的分布。该方法能够区分分布外数据，同时量化不确定性，并在Argoverse 2和INTERACTION数据集上取得了与现有最先进方法相竞争的性能。具体而言，在INTERACTION测试集上实现了0.446米的最小最终位移误差、0.203米的最小平均位移误差以及5.35%的缺失率。此外，本文还提供了广泛的定性与定量分析以评估所提模型。我们的开源代码发布于https://github.com/PurdueDigitalTwin/seneva。