US 11,900,689 B1
Traffic light identification and/or classification for use in controlling an autonomous vehicle
Davis Edward King, Billerica, MA (US); and Yan Li, San Francisco, CA (US)
Assigned to AURORA OPERATIONS, INC., Mountain View, CA (US)
Filed by Aurora Innovation, Inc., Palo Alto, CA (US)
Filed on Sep. 30, 2020, as Appl. No. 17/039,479.
Claims priority of provisional application 63/034,517, filed on Jun. 4, 2020.
Int. Cl. G06V 20/58 (2022.01); G05D 1/02 (2020.01); G05D 1/00 (2006.01); G06V 10/22 (2022.01); G06F 18/214 (2023.01)
CPC G06V 20/584 (2022.01) [G05D 1/0088 (2013.01); G05D 1/0251 (2013.01); G05D 1/0274 (2013.01); G06F 18/214 (2023.01); G06V 10/22 (2022.01); G05D 2201/0213 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method implemented by one or more processors of an autonomous vehicle, the method comprising:
capturing an original image of an environment of the autonomous vehicle, the original image including a plurality of traffic lights;
cropping the original image to generate a cropped image, the cropped image including a region of the environment that includes a given traffic light of the plurality of traffic lights;
determining a configuration that is assigned to the given traffic light, wherein the configuration that is assigned to the given traffic light is one of multiple disparate configurations in a taxonomy of configurations, and wherein the configuration that is assigned to the given traffic light defines an orientation of the given traffic light and a bulb pattern of the given traffic light;
processing the cropped image, that captures the region of the environment that includes the given traffic light, to generate predicted output associated with multiple candidate states of the given traffic light, wherein the predicted output associated with the multiple candidate states of the given traffic light includes a corresponding numerical measure for each of the multiple candidate states for the configuration that is assigned to the given traffic light, and wherein processing the cropped image to generate the predicted output associated with the multiple candidate states of the given traffic light comprises:
selecting, based on the configuration that is assigned to the given traffic light, a machine learning classifier from among a plurality of candidate machine learning classifiers for each of the multiple disparate configurations in the taxonomy of configurations; and
processing, using the machine learning classifier, the cropped image to generate the predicted output associated with the multiple candidate states of the given traffic light;
selecting one of the candidate states as a current state of the given traffic light, wherein the selecting the one of the candidate states as the current state of the given traffic light is based on the predicted output; and
controlling the autonomous vehicle based on the selected current state of the given traffic light.