Systems and methods for training machine-learned models with deviating intermediate representations

A computing system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the system to perform operations, the operations comprising:
• obtaining a plurality of intermediate representations associated with an autonomous vehicle, wherein each intermediate representation is descriptive of at least a portion of a secondary environment proximate to the autonomous vehicle at a plurality of times;
• generating a surrogate machine-learned model based, at least in part, on the plurality of intermediate representations;
• obtaining a target intermediate representation from the plurality of intermediate representations;
• determining an intermediate representation deviation for the target intermediate representation based, at least in part, on the target intermediate representation and the surrogate machine-learned model by:
• obtaining, via the surrogate machine-learned model, one or more ground truth bounding box proposals based, at least in part, on the target intermediate representation,
• obtaining, via the surrogate machine-learned model, one or more deviating bounding box proposals, and
• determining the intermediate representation deviation for the target intermediate representation based, at least in part, on a comparison between the one or more ground truth bound box proposals and the one or more deviating bounding box proposals;
• generating data indicative of a modified intermediate representation based, at least in part, on the target intermediate representation and the intermediate representation deviation;
• and
• communicating the data indicative of the modified intermediate representation to a vehicle computing system associated with the autonomous vehicle.

An autonomous vehicle comprising:

• one or more sensors;
• 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 one or more processors to perform operations, the operations comprising:
• obtaining, via the one or more sensors, sensor data representative of a surrounding environment of the autonomous vehicle;
• generating, via a first portion of a machine-learned model, an intermediate representation based, at least in part, on the sensor data, wherein the intermediate representation is descriptive of at least a portion of the surrounding environment of the autonomous vehicle;
• determining an intermediate representation deviation for the intermediate representation based, at least in part, on the intermediate representation and the machine-learned model, by:
• obtaining, via the machine-learned model, one or more ground truth bounding box proposals based, at least in part, on the intermediate representation,
• obtaining, via the machine-learned model, one or more deviating bounding box proposals, and
• determining the intermediate representation deviation for the intermediate representation based, at least in part, on a comparison between the one or more ground truth bound box proposals and the one or more deviating bounding box proposals;
• generating, data indicative of a modified intermediate representation based, at least in part, on the intermediate representation and the intermediate representation deviation;
• and
• communicating the data indicative of the modified intermediate representation to one or more devices associated with a target autonomous vehicle.