  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.
