US 11,734,473 B2
Perception error models
Sai Anurag Modalavalasa, Foster City, CA (US); Gerrit Bagschik, Foster City, CA (US); Andrew Scott Crego, Foster City, CA (US); Antoine Ghislain Deux, Seattle, WA (US); Rodin Lyasoff, Mountain View, CA (US); James William Vaisey Philbin, Palo Alto, CA (US); Ashutosh Gajanan Rege, San Jose, CA (US); Andreas Christian Reschka, Foster City, CA (US); and Marc Wimmershoff, Palo Alto, CA (US)
Assigned to Zoox, Inc., Foster City, CA (US)
Filed by Zoox, Inc., Foster City, CA (US)
Filed on Dec. 9, 2019, as Appl. No. 16/708,019.
Application 16/708,019 is a continuation in part of application No. 16/586,853, filed on Sep. 27, 2019, granted, now 11,351,995.
Application 16/586,853 is a continuation in part of application No. 16/586,838, filed on Sep. 27, 2019, granted, now 11,625,513.
Prior Publication US 2021/0096571 A1, Apr. 1, 2021
Int. Cl. G06F 30/20 (2020.01); G05D 1/02 (2020.01); G05D 1/00 (2006.01); G07C 5/08 (2006.01); G06V 20/58 (2022.01); G06F 18/23 (2023.01); G06V 20/56 (2022.01)
CPC G06F 30/20 (2020.01) [G05D 1/0027 (2013.01); G05D 1/0088 (2013.01); G05D 1/0214 (2013.01); G06F 18/23 (2023.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G07C 5/0816 (2013.01); G05D 2201/0213 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the system to perform operations comprising:
receiving vehicle data from a vehicle, the vehicle data associated with a state of an object;
receiving ground truth data associated with the object;
determining, based at least in part on the vehicle data and the ground truth data, an error;
determining, based at least in part on the vehicle data, a plurality of parameters;
clustering, as a plurality of clusters and based at least in part on the plurality of parameters and the error, at least a portion of the vehicle data, the clustering comprising associating a distribution of the error with a cluster of the plurality of clusters;
determining classification data identifying a classification of the object;
determining object data identifying an object parameter of the object;
determining an error distribution associated with at least one of a first cluster of the plurality of clusters or a second cluster of the plurality of clusters, the first cluster associated with the classification of the object and the second cluster associated with the object parameter of the object; and
determining, based at least in part on the portion of the vehicle data associated with a cluster of the plurality of clusters, an error model, wherein the error model includes an error distribution associated with at least one of a true positive error or a false positive error.