Systems and methods for object detection at various ranges using multiple range imagery

A computer-implemented method for object detection comprising:

• obtaining, by a computing system comprising one or more computing devices, image data from a camera providing a first field of view;
• inputting, by the computing system, the image data from the camera to a machine-learned model that has been trained to generate object detection range estimates based at least in part on training data representing image data having a second field of view different from the first field of view:
• obtaining, by the computing system, a first range estimate as an output of the machine-learned model, wherein the first range estimate represents an estimate for the second field of view;
• generating, by the computing system, a transformed range estimate by applying a range estimate transform to the first range estimate output from the machine-learned model, wherein the range estimate transform transforms the first range estimate into the transformed range estimate based at least in part on a ratio of focal lengths between the second field of view associated with the training data and the first field of view associated with the camera;
• and
• providing, by the computing system, the transformed range estimate to a motion planning system for use in determining operation of an autonomous vehicle.

An autonomous vehicle comprising:

• a camera providing a first field of view;
• a machine-learned model that has been trained to generate object detection range estimates based at least in part on training data representing image data having a second field of view different from the first field of view;
• and
• a vehicle computing system comprising:
• one or more processors;
• and
• memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• obtaining image data from the camera;
• inputting the image data to the machine-learned model;
• obtaining a first range estimate as an output of the machine-learned model, wherein the first range estimate represents an estimate for the second field of view;
• generating a transformed range estimate by applying a range estimate transform to the first range estimate output from the machine-learned model, wherein the range estimate transform transforms the first range estimate into the transformed range estimate based at least in part on a ratio of focal lengths between the second field of view associated with the training data and the first field of view associated with the camera;
• and
• providing the transformed range estimate to a motion planning system for use in determining operation of the autonomous vehicle.