US 12,222,832 B2
Systems and methods for generating synthetic sensor data via machine learning
Sivabalan Manivasagam, Toronto (CA); Shenlong Wang, Toronto (CA); Wei-Chiu Ma, Toronto (CA); Kelvin Ka Wing Wong, Toronto (CA); Wenyuan Zeng, Toronto (CA); and Raquel Urtasun, Toronto (CA)
Assigned to AURORA OPERATIONS, INC., Pittsburgh, PA (US)
Filed by Aurora Operations, Inc., Pittsburgh, PA (US)
Filed on Sep. 13, 2023, as Appl. No. 18/466,286.
Application 18/466,286 is a continuation of application No. 17/727,085, filed on Apr. 22, 2022, granted, now 11,797,407.
Application 17/727,085 is a continuation of application No. 16/826,990, filed on Mar. 23, 2020, granted, now 11,544,167, issued on Jan. 3, 2023.
Claims priority of provisional application 62/950,279, filed on Dec. 19, 2019.
Claims priority of provisional application 62/936,439, filed on Nov. 16, 2019.
Claims priority of provisional application 62/822,844, filed on Mar. 23, 2019.
Prior Publication US 2023/0418717 A1, Dec. 28, 2023
Int. Cl. G06F 11/263 (2006.01); G06F 17/18 (2006.01); G06N 3/047 (2023.01); G06N 20/00 (2019.01); G06T 15/06 (2011.01); G06T 17/20 (2006.01)
CPC G06F 11/263 (2013.01) [G06F 17/18 (2013.01); G06N 3/047 (2023.01); G06N 20/00 (2019.01); G06T 15/06 (2013.01); G06T 17/20 (2013.01); G06T 2207/10028 (2013.01)] 22 Claims
OG exemplary drawing
 
1. An autonomous vehicle control system for controlling an autonomous vehicle, the autonomous vehicle control system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing:
one or more machine-learned models, wherein at least one of the one or more machine-learned models was tested using synthetic light detection and ranging (LiDAR) data, wherein the synthetic LiDAR data was generated by:
using a physics-based simulation engine to obtain an initial synthetic point cloud,
generating, by a machine-learned model, a value corresponding to a probability that a real LIDAR point cloud would have an error at a point in the initial synthetic point cloud, and
generating, based on a determination of whether, based on the value, to include the error in the synthetic LIDAR data, the synthetic LIDAR data to contain the error at the point; and
instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, the operations comprising:
obtaining LiDAR data descriptive of an environment of the autonomous vehicle;
determining, using the one or more machine-learned models and based on the LiDAR data, a motion plan for controlling the autonomous vehicle; and
controlling the autonomous vehicle according to the motion plan.