  It is critical to predict the motion of surrounding vehicles for self-driving
planning, especially in a socially compliant and flexible way. However, future
prediction is challenging due to the interaction and uncertainty in driving
behaviors. We propose planning-informed trajectory prediction (PiP) to tackle
the prediction problem in the multi-agent setting. Our approach is
differentiated from the traditional manner of prediction, which is only based
on historical information and decoupled with planning. By informing the
prediction process with the planning of ego vehicle, our method achieves the
state-of-the-art performance of multi-agent forecasting on highway datasets.
Moreover, our approach enables a novel pipeline which couples the prediction
and planning, by conditioning PiP on multiple candidate trajectories of the ego
vehicle, which is highly beneficial for autonomous driving in interactive
scenarios.
