US 12,434,709 B1
Adjusting vehicle models based on environmental conditions
Kratarth Goel, Albany, CA (US); Jesse Sol Levinson, Redwood City, CA (US); Derek Xiang Ma, Redwood City, CA (US); Justin Nordgreen, San Francisco, CA (US); Adam Pollack, San Francisco, CA (US); Ekaterina Hristova Taralova, Redwood City, CA (US); and Sarah Tariq, Palo Alto, CA (US)
Assigned to Zoox, Inc., Foster City, CA (US)
Filed by Zoox, Inc., Foster City, CA (US)
Filed on Dec. 18, 2020, as Appl. No. 17/126,312.
This patent is subject to a terminal disclaimer.
Int. Cl. B60W 40/02 (2006.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01)
CPC B60W 40/02 (2013.01) [G06N 20/00 (2019.01); G06V 20/56 (2022.01); B60W 2420/403 (2013.01); B60W 2420/54 (2013.01); B60W 2555/20 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising:
receiving, from an ambient light sensor of an autonomous vehicle, ambient light data representing an amount of ambient light, at a first time, within an environment in which the autonomous vehicle is operating;
determining, based at least in part on the ambient light data at the first time, that the amount of ambient light within the environment has changed from a second time prior to the first time;
determining, as a similarity, a subset of weights associated with a current configuration of a machine learned model and also associated with a different configuration of the machine learned model;
reconfiguring, based at least in part on the change in the amount of ambient light within the environment and the similarity, the current configuration of the machine learned model to include a subset of weights associated with the different configuration of the machine learned model and not previously associated with the current configuration of the machine learned model, the current configuration of the machine learned model and the different configuration of the machine learned model sharing a same machine learned model architecture associated with multiple available sets of weights;
wherein reconfiguring the current configuration of the machine learned model includes determining a first confidence level associated with data representing a change in the amount of ambient light within the environment;
receiving, from an image sensor associated with the autonomous vehicle, image data representing a portion of the environment;
inputting the image data into the machine learned model; and
performing an action associated with at least one of the machine learned model or an output of the machine learned model.