Systems and methods for generating motion forecast data for actors with respect to an autonomous vehicle and training a machine learned model for the same

A computer-implemented method for forecasting actor motion data:

• inputting, by a computing system comprising one or more computing devices, sensor data into one or more object detection models configured to receive sensor data, and in response to receipt of the sensor data, generate object detection data describing locations of a plurality of actors relative to an autonomous vehicle;
• receiving, by the computing system and as an output of the one or more object detection models, the object detection data describing the location of the plurality of the actors relative to the autonomous vehicle;
• inputting, by the computing system, the object detection data into a graph neural network comprising a plurality of nodes and a plurality of edges, the graph neural network configured to receive the object detection data, and in response to receipt of the object detection data, output motion forecast data with respect to the plurality of actors;
• iteratively updating, by the computing system, a plurality of node states respectively associated with the plurality of nodes;
• receiving, by the computing system and as an output of the graph neural network, the motion forecast data with respect to the plurality of actors;
• determine a motion plan for the autonomous vehicle based at least in part on the motion forecast data;
• and
• control the autonomous vehicle based at least in part on the motion plan.

A computer-implemented method for training a graph neural network for generating actor motion forecast data, the method comprising:

• inputting, by a computing system comprising one or more computing devices, sensor data into one or more object detection models configured to receive the sensor data, and in response to receipt of the sensor data, output object detection data describing locations of a plurality of actors relative to an autonomous vehicle;
• receiving, by the computing system and as an output of the one or more object detection models, the object detection data describing the location of the plurality of the actors relative to the autonomous vehicle;
• inputting, by the computing system, the object detection data into a graph neural network comprising a plurality of nodes and a plurality of edges, the graph neural network configured to receive the object detection data, and in response to receipt of the object detection data, output motion forecast data with respect to the plurality of actors;
• iteratively updating, by the computing system, a plurality of node states respectively associated with the plurality of nodes;
• receiving, by the computing devices and as an output of the graph neural network, the motion forecast data with respect to the plurality of actors;
• adjusting, by the computing system, at least one parameter of the graph neural network based on a comparison of the motion forecast data with respect to ground truth motion forecast data;
• determine a motion plan for the autonomous vehicle based at least in part on the motion forecast data;
• and
• control the autonomous vehicle based at least in part on the motion plan.