Goal-based motion forecasting

A computing system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
• (a) obtaining state data associated with a plurality of actors within an environment of an autonomous vehicle and map data indicating a plurality of lanes of the environment;
• (b) determining a plurality of potential goals comprising at least one potential goal for each actor of the plurality of actors based on the state data and the map data, wherein a potential goal comprises a potential destination location in the environment and a goal path to the potential destination location;
• (c) processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to generate a plurality of feature embeddings for respective nodes in a graph neural network corresponding to the plurality of actors and the plurality of potential goals, the plurality of feature embeddings indicative of respective distances between a set of the plurality of actors and a shared space distinct from spaces corresponding to the plurality of actors and corresponding to an intersection of goal paths associated with the set of the plurality of actors;
• (d) processing the plurality of feature embeddings to determine a forecasted goal for a respective actor of the plurality of actors and a continuous trajectory for the respective actor based on the forecasted goal;
• and
• (e) initiating a motion of the autonomous vehicle based on the forecasted goal and the continuous trajectory of the respective actor.

A control system for an autonomous vehicle, the control system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
• (a) obtaining state data associated with a plurality of actors within an environment of an autonomous vehicle and map data indicating a plurality of lanes of the environment;
• (b) determining a plurality of potential goals comprising at least one potential goal for each actor of the plurality of actors based on the state data and the map data, wherein a potential goal comprises a potential destination location in the environment and a goal path to the potential destination location;
• (c) processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to generate a plurality of feature embeddings for respective nodes in a graph neural network corresponding to the plurality of actors and the plurality of potential goals, the plurality of feature embeddings indicative of respective distances between a set of the plurality of actors and a shared space distinct from spaces corresponding to the plurality of actors and corresponding to an intersection of goal paths associated with the set of the plurality of actors;
• (d) processing the plurality of feature embeddings to determine a forecasted goal for a respective actor of the plurality of actors and a continuous trajectory for the respective actor based on the forecasted goal;
• and
• (e) initiating a motion of the autonomous vehicle based on the forecasted goal and the continuous trajectory of the respective actor.