US 12,333,823 B1
Machine-learned model training for inferring object attributes from distortions in temporal data
Scott M. Purdy, Lake Forest Park, WA (US)
Assigned to Zoox, Inc., Foster City, CA (US)
Filed by Zoox, Inc., Foster City, CA (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,756.
Int. Cl. G06V 20/58 (2022.01); B60W 60/00 (2020.01); G06V 10/764 (2022.01)
CPC G06V 20/58 (2022.01) [B60W 60/001 (2020.02); G06V 10/764 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
receiving sensor data associated with an object in an environment, the sensor data generated by a temporal sensor of a vehicle, wherein a movement of the object or the vehicle contributes to a distortion in a representation of the object in the sensor data relative to an actual shape of the object;
generating time-dimensional sensor data based on the sensor data, the time-dimensional sensor data indicative of the movement of the object through the environment over a period of time;
inputting the time-dimensional sensor data into a machine-learned model;
receiving an output from the machine-learned model, the output including a predicted velocity of the object;
determining a difference between the predicted velocity of the object and a measured velocity of the object; and
based at least in part on the difference meeting or exceeding a threshold difference, altering a parameter of the machine-learned model to minimize the difference and obtain a trained machine-learned model.