US 11,748,620 B2
Generating ground truth for machine learning from time series elements
Ashok Kumar Elluswamy, Sunnyvale, CA (US); Matthew Bauch, San Francisco, CA (US); Christopher Payne, San Francisco, CA (US); Andrej Karpathy, San Francisco, CA (US); and Joseph Polin, San Francisco, CA (US)
Assigned to Tesla, Inc., Austin, TX (US)
Filed by Tesla, Inc., Austin, TX (US)
Filed on Apr. 20, 2021, as Appl. No. 17/301,965.
Application 17/301,965 is a continuation of application No. 16/265,729, filed on Feb. 1, 2019, granted, now 10,997,461.
Prior Publication US 2021/0342637 A1, Nov. 4, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G05D 1/02 (2020.01); G06V 20/56 (2022.01); G06F 18/28 (2023.01); G16Y 20/10 (2020.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G05D 1/0221 (2013.01); G06F 18/28 (2023.01); G06V 20/588 (2022.01); G16Y 20/10 (2020.01); G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method implemented by one or more processors, wherein the method comprises:
obtaining sensor data captured at respective times within a period of time;
determining a ground truth based on the sensor data, the ground truth comprising a three-dimensional feature associated with the sensor data; and
training a machine learning model using a training dataset comprising the determined ground truth and a portion of the sensor data captured at a particular time within the period of time, wherein the machine learning model is trained to output the ground truth based on an input of the portion of the sensor data.