US 12,449,813 B1
Training machine learning model for controlling autonomous vehicle
Arun Venkatraman, Mountain View, CA (US); and James Andrew Bagnell, Pittsburgh, PA (US)
Assigned to Aurora Operations, Inc, Mountain View, CA (US)
Filed by Aurora Operations, Inc., Mountain View, CA (US)
Filed on Sep. 27, 2023, as Appl. No. 18/373,670.
Application 18/373,670 is a continuation of application No. 17/137,100, filed on Dec. 29, 2020, granted, now 11,782,451.
Claims priority of provisional application 63/013,269, filed on Apr. 21, 2020.
Int. Cl. G05D 1/00 (2024.01); B60W 60/00 (2020.01)
CPC G05D 1/0221 (2013.01) [B60W 60/001 (2020.02); B60W 2540/30 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for training an autonomous vehicle control system of an autonomous vehicle, the method implemented by one or more processors and comprising:
obtaining a first instance of manual driving data corresponding to a first vehicle being driven manually by a manual driver,
wherein the first instance of manual driving data defines: a trajectory of the first vehicle controlled by a manual driver, and one or more aspects of an environment of the first vehicle while following the trajectory;
processing by the autonomous vehicle control system used to control a second vehicle, the first instance of manual driving data including the one or more aspects of the environment of the first instance of manual driving data, to generate output indicating a predicted trajectory as determined by the control system of the second vehicle;
comparing the predicted trajectory of the second vehicle with the trajectory of the first vehicle, to determine a deviation between the predicted trajectory of the second vehicle and the trajectory of the first vehicle;
identifying a statistically significant deviation between the predicted trajectory of the second vehicle with the trajectory of the first vehicle; and
determining whether the deviation satisfies one or more metrics based upon repeated similar prior instances of obtained trajectory of the first vehicle;
based on determining that the deviation satisfies the one or more metrics, generating a training instance comprising the first instance of manual driving data; and
training the autonomous vehicle control system using the generated training instance.