  Behavior prediction plays an important role in integrated autonomous driving
software solutions. In behavior prediction research, interactive behavior
prediction is a less-explored area, compared to single-agent behavior
prediction. Predicting the motion of interactive agents requires initiating
novel mechanisms to capture the joint behaviors of the interactive pairs. In
this work, we formulate the end-to-end joint prediction problem as a sequential
learning process of marginal learning and joint learning of vehicle behaviors.
We propose ProspectNet, a joint learning block that adopts the weighted
attention score to model the mutual influence between interactive agent pairs.
The joint learning block first weighs the multi-modal predicted candidate
trajectories, then updates the ego-agent's embedding via cross attention.
Furthermore, we broadcast the individual future predictions for each
interactive agent into a pair-wise scoring module to select the top $K$
prediction pairs. We show that ProspectNet outperforms the Cartesian product of
two marginal predictions, and achieves comparable performance on the Waymo
Interactive Motion Prediction benchmarks.
