US 12,394,213 B2
Polygon sequence matching for label association in autonomous vehicle logs
Zachary Kurtz, Pittsburgh, PA (US)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US)
Filed on Jan. 6, 2023, as Appl. No. 18/094,133.
Prior Publication US 2024/0233459 A1, Jul. 11, 2024
Int. Cl. G06V 20/58 (2022.01); G06F 16/783 (2019.01); G06N 20/00 (2019.01); G06T 7/292 (2017.01); G06V 20/40 (2022.01); G06V 20/56 (2022.01); G06N 3/08 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G07C 5/08 (2006.01); H04N 23/61 (2023.01)
CPC G06V 20/58 (2022.01) [G06F 16/7837 (2019.01); G06N 20/00 (2019.01); G06T 7/292 (2017.01); G06V 20/46 (2022.01); G06V 20/48 (2022.01); G06V 20/56 (2022.01); G06N 3/08 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/30252 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G07C 5/08 (2013.01); H04N 23/61 (2023.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for associating labels in autonomous vehicle logs, the computer-implemented method comprising:
retrieving a logged track from one or more computing devices of an autonomous vehicle (AV) system, wherein the logged track comprises a sequence of logged frames;
retrieving a plurality of labeled tracks from a database, wherein each labeled track comprises a sequence of labeled frames;
for each logged frame in the logged track:
identifying a logged polygon in the respective logged frame; and for each labeled track of the plurality of labeled tracks:
retrieving a labeled frame that has a labeled timestamp within a predetermined time window around a logged timestamp of the logged polygon identified in the respective logged frame;
generating a similarity score based on a comparison of the logged polygon identified in the respective logged frame and a labeled polygon in the labeled frame;
identifying the respective logged frame and the labeled frame as a match-frame based on a comparison of the similarity score to a first user-defined threshold;
generating a candidate pair for each identified match-frame comprising the logged track and the labeled track associated with the identified match-frame;
removing one or more duplicate candidate pairs;
generating a match quality score for each of the candidate pairs;
generating a match quality score percentage for each of the candidate pairs by:
identifying the labeled track in the respective candidate pair;
identifying other candidate pairs that include the identified labeled track;
computing a sum of the match quality scores for the other candidate pairs that include the identified labeled track; and
dividing the match quality score for the respective candidate pair by the sum of the match quality scores for the other candidate pairs that include the identified labeled track; and
displaying the candidate pair based on a comparison of the match quality score to a second user-defined threshold.