Vehicle control systems and methods using kinematically stabilized machine-learning model predicted controls

Abstract

A method for controlling a vehicle based on a kinematically stable trajectory is described. The method includes receiving sensor data, from one or more sensor of an ego-vehicle configured to perceive an environment around the ego-vehicle; predicting, with a machine learning model configured to ingest the sensor data, an acceleration and a curvature defining a trajectory for the ego-vehicle; evaluating the acceleration and the curvature with a kinematics decoder configured to determine that the acceleration and the curvature are kinematically feasible for the ego-vehicle; generating, with the kinematics decoder, one or more outputs based on evaluation of the acceleration and the curvature, wherein the one or more outputs define the kinematically stable trajectory for controlling the ego-vehicle; and controlling the ego-vehicle based on the one or more outputs.