Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems

This work introduces a flexible architecture for real-time occupancy forecasting. In contrast to existing, more computationally expensive architectures, the proposed model exploits recursive latent state estimation, using learned transformer-based prediction and update modules. This allows for highly efficient real-time inference on an embedded system (profiled on an Nvidia Xavier AGX), and the inclusion of a broad set of information from a diverse set of sensors. The architecture is able to process sparse and occluded observations of agent positions and scene context as this is made available, and does not require motion tracklet inputs. \networkName{} accomplishes this by encoding the scene into a latent state that evolves in time with self-attention and is updated with contextual information such as traffic signals, road topology or agent detections using cross-attention. Occupancy predictions are made by sparsely querying positions of interest as opposed to generating a fixed size raster image, which allows for variable resolution occupancy prediction or local querying by downstream trajectory optimisation algorithms, saving computational effort.