US 12,351,205 B2
Lane segment clustering using hybrid distance metrics
Andrew Hartnett, West Hartford, CT (US); George Peter Kenneth Carr, Allison Park, PA (US); and Nikolai Popov, Munich (DE)
Assigned to Volkswagen Group of America Investments, LLC, Reston, VA (US)
Filed by ARGO AI, LLC, Pittsburgh, PA (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,630.
Prior Publication US 2023/0415766 A1, Dec. 28, 2023
Int. Cl. G06F 18/23 (2023.01); B60W 60/00 (2020.01); G01C 21/34 (2006.01); G06V 20/56 (2022.01)
CPC B60W 60/001 (2020.02) [G01C 21/3415 (2013.01); G01C 21/3461 (2013.01); G06V 20/588 (2022.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2552/53 (2020.02)] 20 Claims
OG exemplary drawing
 
8. A system, comprising:
a memory; and
at least one processor coupled to the memory and configured to execute operations comprising:
grouping a set of lane segments based on trained labels from a supervised learning process, wherein the trained labels are applicable to a plurality of features of the set of lane segments,
calculating distances corresponding to the set of lane segments based on individual features of the set of lane segments assessed by a metric learning model trained with the trained labels,
clustering the set of lane segments based on the distances, and
assigning corresponding protolanes to clusters of the set of lane segments based on the clustering, wherein an additional lane segment would be assigned to one of the protolanes based on clustering the additional lane segment into a corresponding one of the clusters.