US 12,151,707 B1
Learned validation metric for evaluating autonomous vehicle motion planning performance
James Andrew Bagnell, Pittsburgh, PA (US); Brian Christopher Becker, Pittsburgh, PA (US); Davis Edward King, Billerica, MA (US); Skandavimal Shridhar, Pittsburgh, PA (US); Drew Edward Steedly, Kirkland, WA (US); and Xinyan Yan, El Paso, TX (US)
Assigned to AURORA OPERATIONS, INC., Pittsburgh, PA (US)
Filed by Aurora Operations, Inc., Pittsburgh, PA (US)
Filed on Apr. 11, 2024, as Appl. No. 18/633,191.
Claims priority of provisional application 63/615,853, filed on Dec. 29, 2023.
Int. Cl. B60W 60/00 (2020.01)
CPC B60W 60/001 (2020.02) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for validating a trajectory generated by an autonomous vehicle control system (“the AV trajectory”) in a driving scenario, comprising:
(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.