公开(公告)号：US12202512B1

公开(公告)日：2025-01-21

申请号：US18628336

申请日：2024-04-05

Applicant: Aurora Operations, Inc.

Inventor： Davis Edward King

IPC: B60W60/00 ,  B60W50/06

Abstract: An example method includes (a) obtaining an object detection from a perception system that describes an object in an environment of the autonomous vehicle; (b) obtaining, from a reference dataset, a label that describes a reference position of the object in the environment; (c) determining a plurality of component divergence values respectively for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the object detection and the label; (d) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the object detection and the label, wherein the machine-learned model includes a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; (e) evaluating a quality of a match between the object detection and the label based on the score.

公开(公告)号：US12151707B1

公开(公告)日：2024-11-26

申请号：US18633191

申请日：2024-04-11

Applicant: Aurora Operations, Inc.

Inventor： James Andrew Bagnell ,  Brian Christopher Becker ,  Davis Edward King ,  Skandavimal Shridhar ,  Drew Edward Steedly ,  Xinyan Yan

IPC: B60W60/00

Abstract: The present disclosure provides an example method for validating a trajectory generated by an autonomous vehicle control system (AV trajectory) in a driving scenario. The example method includes (a) obtaining the AV trajectory and a reference trajectory, wherein the reference trajectory describes a desired motion of a vehicle in the driving scenario; (b) determining a plurality of component divergence values for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the AV trajectory and the reference trajectory; (c) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the AV trajectory and the reference trajectory, wherein the machine-learned model comprises a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; and (d) validating the AV trajectory based on the score.