Systems and methods for training predictive models for autonomous devices

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 comprising:
• obtaining a rasterized image from a set of training data;
• generating a predicted trajectory for a training object by inputting the rasterized image into a machine-learned prediction generator model;
• converting the predicted trajectory into a rasterized trajectory, wherein the rasterized trajectory spatially corresponds to the rasterized image;
• determining a score for the predicted trajectory by inputting the rasterized trajectory and the rasterized image into a machine-learned discriminator model configured to output the score for the predicted trajectory;
• determining an overall for the machine-learned prediction generator model based, at least in part, on the loss score;
• and
• training the machine-learned prediction generator model by minimizing the overall loss for the machine-learned prediction generator model.

A computing system, comprising:

• a machine-learned prediction generator model configured to receive a rasterized image associated with a training object, and in response to receipt of the rasterized image, output a predicted trajectory of the training object;
• a differential rasterizer configured to receive a predicted trajectory, and in response to receipt of the predicted trajectory, convert the predicted trajectory into a rasterized trajectory;
• a machine-learned discriminator model configured to receive the rasterized trajectory and the rasterized image, and in response to receipt of the rasterized trajectory and rasterized image, output a score for the predicted trajectory;
• a memory that stores a set of instructions;
• and
• one or more processors which use the set of instructions to:
• receive the rasterized image associated with the training object;
• generate the predicted trajectory of the training object by inputting the rasterized image into the machine-learned prediction generator model;
• convert the predicted trajectory into the rasterized trajectory by inputting the predicted trajectory into the differential rasterizer;
• determine the score for the predicted trajectory by inputting the rasterized trajectory and the rasterized image into the machine-learned discriminator model;
• determine an overall loss for the machine-learned prediction generator model based, at least in part, on the score;
• and
• train the machine-learned prediction generator model by minimizing the overall loss for the machine-learned prediction generator model.