Systems and Methods for Using Attention Masks to Improve Motion Planning

A computing system for improving autonomous vehicle motion planning for an autonomous vehicle, the system comprising:

• one or more processors and one or more non-transitory computer-readable memories;
• wherein the one or more non-transitory computer-readable memories store instructions that, when executed by the processor, cause the computing system to perform operations, the operations comprising:
• accessing sensor data and map data for an area around the autonomous vehicle;
• generating a voxel grid representation of the sensor data and map data;
• generating an attention mask based on the voxel grid representation;
• generating, by using the voxel grid representation and the attention mask as input to a machine-learned model, an attention weighted feature map;
• determining, by using the attention weighted feature map, a planning cost volume for an area around the autonomous vehicle;
• and
• selecting a trajectory for the autonomous vehicle based on the planning cost volume.

A computer-implemented method for training a model, comprising:

• providing, by a computing system with one or more processors, a set of training sensor data as input to one or more machine-learned models, the machine-learned models comprising a set of weights;
• receiving, by the computing system, as output from the one or more machine-learned models, perception data and a binary attention mask;
• generating, by the computing system, perception loss data by determining a difference between the perception data received from the one or more machine-learned models and predetermined ground truth data associated with the training sensor data;
• modifying, by the computing system, the perception loss data based on the binary attention mask;
• and
• updating, by the computing system, the set of weights included in the one or more machine-learned models based on the modified perception loss data.