Systems and methods for heterogeneous multi-agent multi-modal trajectory prediction with evolving interaction graphs

Abstract

The systems and methods herein utilize interactive Gaussian processes for crowd navigation. For example, an encoder receives sensor data and context information. The encoder also extracts interaction patterns from observed trajectories from the sensor data and context information. The encoder further generates a static latent interaction graph for a first time step based on the interaction patterns. A recurrent module generates a distribution of time dependent static latent interaction graphs iteratively from the first time step for a series of time steps based on the static latent interaction graph. The series of time steps are separated by a re-encoding gap. The decoder generates multi-modal distribution of future states based on the distribution of time dependent static latent interaction graphs.