US 11,816,585 B2
Machine learning models operating at different frequencies for autonomous vehicles
Anting Shen, Mountain View, CA (US)
Assigned to Tesla, Inc., Austin, TX (US)
Filed by Tesla, Inc., Austin, TX (US)
Filed on Dec. 3, 2019, as Appl. No. 16/701,669.
Claims priority of provisional application 62/774,793, filed on Dec. 3, 2018.
Prior Publication US 2020/0175401 A1, Jun. 4, 2020
Int. Cl. G06N 20/10 (2019.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01)
CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A method implemented by a system of one or more processors, the method comprising:
obtaining a plurality of images at a threshold frequency, the images being obtained from one or more image sensors positioned about a vehicle;
determining, based on the images, location information associated with objects classified in the images, wherein determining location information is based on analyzing the images via a first machine learning model at the threshold frequency,
wherein a subset of the images is analyzed via a second machine learning model at less than the threshold frequency,
wherein the first machine learning model is configured to periodically receive output information from the second machine learning model, the received output information being input into the first machine learning model in combination with a first image of the plurality of images, and the received output information being usable to increase an accuracy of determining location information associated with objects classified in the first image,
wherein the plurality of images represents a sequence of images comprising at least the first image, and wherein prior to completion of the analysis by the second machine learning model, the first image is analyzed via the first machine learning model; and
outputting the determined location information, wherein the determined location information is configured for use in autonomous driving of the vehicle.