  For an autonomous vehicle to plan a path in its environment, it must be able
to accurately forecast the trajectory of all dynamic objects in its proximity.
While many traditional methods encode observations in the scene to solve this
problem, there are few approaches that consider the effect of the ego vehicle's
behavior on the future state of the world. In this paper, we introduce VRD, a
vectorized world model-inspired approach to the multi-agent motion forecasting
problem. Our method combines a traditional open-loop training regime with a
novel dreamed closed-loop training pipeline that leverages a kinematic
reconstruction task to imagine the trajectory of all agents, conditioned on the
action of the ego vehicle. Quantitative and qualitative experiments are
conducted on the Argoverse 2 multi-world forecasting evaluation dataset and the
intersection drone (inD) dataset to demonstrate the performance of our proposed
model. Our model achieves state-of-the-art performance on the single prediction
miss rate metric on the Argoverse 2 dataset and performs on par with the
leading models for the single prediction displacement metrics.
