US 12,480,781 B2
Crowd-sourcing lane line maps for a vehicle
Bo Yu, Troy, MI (US); Gui Chen, Sterling Heights, MI (US); Fan Bai, Ann Arbor, MI (US); Shuqing Zeng, Sterling Heights, MI (US); Joon Hwang, Pflugerville, TX (US); and Mason David Gemar, Cedar Park, TX (US)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM Global Technology Operations LLC, Detroit, MI (US)
Filed on Jul. 26, 2023, as Appl. No. 18/359,017.
Prior Publication US 2025/0035463 A1, Jan. 30, 2025
Int. Cl. B60W 60/00 (2020.01); G01C 21/00 (2006.01)
CPC G01C 21/3815 (2020.08) [B60W 60/001 (2020.02); G01C 21/3841 (2020.08); G01C 21/3896 (2020.08); B60W 2552/53 (2020.02); B60W 2556/40 (2020.02); B60W 2556/45 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method for crowd-sourcing lane line map data for a vehicle, the method comprising:
receiving a plurality of observations from one or more vehicles, wherein each of the plurality of observations includes a vehicle location trajectory and a plurality of points positioned relative to the vehicle location trajectory, and wherein each of the plurality of points corresponds to an object in an environment surrounding the one or more vehicles;
classifying the plurality of observations into a plurality of observation categories, wherein each of the plurality of observation categories includes at least one of the plurality of observations, wherein each of the plurality of observation categories includes a subset of the plurality of observations having at least one observation characteristic in common;
subsequent to classifying the plurality of observations into the plurality of observation categories, shifting the subset of the plurality of observations in each of the plurality of observation categories to generate a plurality of point clouds, wherein one of the plurality of point clouds corresponds to each of the plurality of observation categories;
subsequent to generating the plurality of point clouds, generating a plurality of aligned point clouds by aligning each of the plurality of point clouds to account for location drift introduced when shifting the subset of the plurality of observations in each of the plurality of observation categories;
determining a plurality of lane line maps based at least in part on the plurality of aligned point clouds, wherein one of the plurality of lane line maps corresponds to each of the plurality of aligned point clouds;
updating a map database based at least in part on the plurality of lane line maps; and
adjusting a path of the vehicle using an automated driving system based at least in part on the plurality of lane line maps.