US 12,367,086 B2
Generating autonomous vehicle simulation data from logged data
John Michael Wyrwas, Cupertino, CA (US); Jessica Elizabeth Smith, Pittsburgh, PA (US); and Simon Box, San Francisco, CA (US)
Assigned to Aurora Operations, Inc., Pittsburgh, PA (US)
Filed by Aurora Innovation, Inc., Palo Alto, CA (US)
Filed on Dec. 11, 2020, as Appl. No. 17/119,214.
Claims priority of provisional application 62/988,303, filed on Mar. 11, 2020.
Prior Publication US 2021/0286924 A1, Sep. 16, 2021
Int. Cl. G06F 11/07 (2006.01); G05D 1/00 (2024.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 30/15 (2020.01); G06F 30/27 (2020.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G07C 5/00 (2006.01); G07C 5/08 (2006.01)
CPC G06F 11/0739 (2013.01) [G06F 18/2155 (2023.01); G06F 18/2185 (2023.01); G06F 30/15 (2020.01); G06F 30/27 (2020.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G07C 5/008 (2013.01); G07C 5/0841 (2013.01); G05D 1/0088 (2013.01); G05D 1/0221 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating simulation data, the method comprising:
receiving logged data including that of from one or more autonomous driving subsystems of an autonomous vehicle;
generating augmented data from the logged data, the augmented data describing an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic;
generating a simulation scenario as the simulation data, the simulation scenario generated from the augmented data;
training a machine learning model of the autonomous vehicle using the simulation data by:
executing a simulation based on the simulation scenario to generate a simulated output;
providing the simulation scenario as a training input to the machine learning model to generate a predicted output of the machine learning model; and
updating one or more weights in the machine learning model based on a difference between the predicted output and the simulated output of the simulation scenario; and
performing, using the trained machine learning model, an autonomous vehicle task corresponding to autonomous driving by the autonomous vehicle during a real-world operation of the autonomous vehicle.