  Multi-agent motion prediction is a crucial concern in autonomous driving, yet
it remains a challenge owing to the ambiguous intentions of dynamic agents and
their intricate interactions. Existing studies have attempted to capture
interactions between road entities by using the definite data in history
timesteps, as future information is not available and involves high
uncertainty. However, without sufficient guidance for capturing future states
of interacting agents, they frequently produce unrealistic trajectory overlaps.
In this work, we propose Future Interaction modeling for Motion Prediction
(FIMP), which captures potential future interactions in an end-to-end manner.
FIMP adopts a future decoder that implicitly extracts the potential future
information in an intermediate feature-level, and identifies the interacting
entity pairs through future affinity learning and top-k filtering strategy.
Experiments show that our future interaction modeling improves the performance
remarkably, leading to superior performance on the Argoverse motion forecasting
benchmark.
