Object motion prediction and autonomous vehicle control

A computing system, comprising:

• one or more processors;
• and
• one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
• obtaining state data associated with an object that is within a surrounding environment of an autonomous vehicle;
• obtaining data associated with a geographic area in which the object is located;
• generating image data associated with the object based at least in part on a fusion of the state data associated with the object and the data associated with the geographic area in which the object is located;
• determining a plurality of predicted trajectories of the object based at least in part on the image data associated with the object and a machine-learned model, at least one of the plurality of predicted trajectories comprising a plurality of predicted waypoints, wherein at least one of the plurality of predicted waypoints comprises a respective indicator, the respective indicator associated with a predicted waypoint with a particular size indicative of a waypoint confidence level for the predicted waypoint;
• determining a selected predicted trajectory of the object from among the plurality of predicted trajectories of the object;
• and
• planning a motion of the autonomous vehicle based at least in part on the selected predicted trajectory of the object.

An autonomous vehicle comprising:

• one or more processors;
• and
• one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the autonomous vehicle to perform operations, the operations comprising:
• obtaining state data associated with an object that is within a surrounding environment of the autonomous vehicle;
• obtaining data associated with a geographic area in which the object is located;
• generating a combined data set associated with the object based at least in part on the state data associated with the object and the data associated with the geographic area in which the object is located, wherein the combined data set comprises one or more rasterized images that include the object, wherein the one or more rasterized images is indicative of the state data associated with the object and the data associated with the geographic area in which the object is located at various times;
• and
• determining a plurality of predicted trajectories of the object based at least in part on the one or more rasterized images that include the object and a machine-learned model, wherein at least one of the predicted trajectories comprises a plurality of predicted waypoints of the object, wherein at least one of the plurality of predicted waypoints comprises a respective indicator, the respective indicator associated with a predicted waypoint with a particular size indicative of a waypoint confidence level for the predicted waypoint.