  This work introduces a novel and adaptable architecture designed for
real-time occupancy forecasting that outperforms existing state-of-the-art
models on the Waymo Open Motion Dataset in Soft IOU. The proposed model uses
recursive latent state estimation with learned transformer-based functions to
effectively update and evolve the state. This enables highly efficient
real-time inference on embedded systems, as profiled on an Nvidia Xavier AGX.
Our model, MotionPerceiver, achieves this by encoding a scene into a latent
state that evolves in time through self-attention mechanisms. Additionally, it
incorporates relevant scene observations, such as traffic signals, road
topology and agent detections, through cross-attention mechanisms. This forms
an efficient data-streaming architecture, that contrasts with the expensive,
fixed-sequence input common in existing models. The architecture also offers
the distinct advantage of generating occupancy predictions through localized
querying based on a point-of-interest, as opposed to generating fixed-size
occupancy images that render potentially irrelevant regions.
