  Motion forecasting in highly interactive scenarios is a challenging problem
in autonomous driving. In such scenarios, we need to accurately predict the
joint behavior of interacting agents to ensure the safe and efficient
navigation of autonomous vehicles. Recently, goal-conditioned methods have
gained increasing attention due to their advantage in performance and their
ability to capture the multimodality in trajectory distribution. In this work,
we study the joint trajectory prediction problem with the goal-conditioned
framework. In particular, we introduce a
conditional-variational-autoencoder-based (CVAE) model to explicitly encode
different interaction modes into the latent space. However, we discover that
the vanilla model suffers from posterior collapse and cannot induce an
informative latent space as desired. To address these issues, we propose a
novel approach to avoid KL vanishing and induce an interpretable interactive
latent space with pseudo labels. The proposed pseudo labels allow us to
incorporate domain knowledge on interaction in a flexible manner. We motivate
the proposed method using an illustrative toy example. In addition, we validate
our framework on the Waymo Open Motion Dataset with both quantitative and
qualitative evaluations.
