  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.
