Vehicle control using auto-regressive control distibution generation based on world state embeddings

Abstract

A machine-learned architecture for generating a single trajectory or multiple trajectories for controlling a vehicle may comprise an embedding model that generates an embedding of a world state indicating environment, object, and/or other states, one or more machine-learned layers that determine a predicted world state embedding using the world state embedding, and concatenating, as combined data, that predicted world state embedding to a steering angle distribution and a velocity distribution. The combined data is provided as input to a machine-learned model (that may be a single machine-learned model or may comprise two separate machine-learned models) that determines a next steering angle distribution and a next velocity distribution. These distributions may be used as part of generating one or more trajectories for controlling the vehicle. The architecture may be iteratively used to create a series of distributions that are used to create one or more trajectories.