US 12,214,801 B2
Generating autonomous vehicle testing data through perturbations and adversarial loss functions
Jingkang Wang, Toronto (CA); Ava Alison Pun, Toronto (CA); Xuanyuan Tu, Milton (CA); Mengye Ren, Toronto (CA); Abbas Sadat, Toronto (CA); Sergio Casas, Toronto (CA); Sivabalan Manivasagam, 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 Nov. 17, 2021, as Appl. No. 17/528,549.
Claims priority of provisional application 63/114,782, filed on Nov. 17, 2020.
Prior Publication US 2022/0153298 A1, May 19, 2022
Int. Cl. B60W 60/00 (2020.01); G06N 3/086 (2023.01)
CPC B60W 60/0011 (2020.02) [B60W 60/0013 (2020.02); B60W 60/00276 (2020.02); G06N 3/086 (2013.01); B60W 2420/408 (2024.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating testing data for an autonomous vehicle, the method comprising:
(a) obtaining sensor data descriptive of a traffic scenario comprising a subject vehicle and one or more actors in an environment of the subject vehicle;
(b) generating a perturbed trajectory for a first actor of the one or more actors in the environment based on one or more perturbation values that are selected from a defined perturbation search space by: creating a set of physically feasible trajectories for the first actor; generating an initial perturbed trajectory for the first actor based on the one or more perturbation values; and projecting the initial perturbed trajectory onto the set of physically feasible trajectories to generate the perturbed trajectory;
(c) generating simulated sensor data comprising data descriptive of the perturbed trajectory for the first actor in the environment;
(d) providing the simulated sensor data as input to an autonomous vehicle control system configured to process the simulated sensor data to generate an updated trajectory for the subject vehicle in the environment; and
(e) optimizing an adversarial loss function based on the updated trajectory for the subject vehicle to generate an adversarial loss value.