US 12,243,327 B2
Generation and update of HD maps using data from heterogeneous sources
Gil Arditi, Palo Alto, CA (US)
Assigned to Lyft, Inc., San Francisco, CA (US)
Filed by Lyft, Inc., San Francisco, CA (US)
Filed on Nov. 11, 2022, as Appl. No. 18/054,844.
Application 18/054,844 is a continuation of application No. 15/811,489, filed on Nov. 13, 2017, granted, now 11,537,868.
Prior Publication US 2023/0132889 A1, May 4, 2023
Int. Cl. G06V 20/58 (2022.01); G01C 21/00 (2006.01); G01C 21/32 (2006.01); G05D 1/00 (2024.01); G06F 16/29 (2019.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01)
CPC G06V 20/58 (2022.01) [G01C 21/32 (2013.01); G01C 21/3841 (2020.08); G01C 21/3878 (2020.08); G05D 1/0274 (2013.01); G06F 16/29 (2019.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising, by a computing system:
collecting, by the computing system of a first vehicle, first sensor data, of a particular geographic location, from a first type of sensor of the first vehicle associated with a fleet of vehicles;
processing, by the computing system of the first vehicle, the first sensor data to identify one or more first objects at the particular geographic location;
accessing, by the computing system of the first vehicle, an existing high-definition (HD) map associated with the particular geographic location;
determining, by the computing system of the first vehicle, whether the one or more first objects are included in the existing HD map; and
in response to determining that the one or more first objects are not included in the existing HD map, sending, by the computing system of the first vehicle, the first sensor data to a server to transform the first sensor data into a latent representation, wherein transforming the first sensor data into the latent representation comprises:
encoding, using a trained machine-learning model, the first sensor data collected from the first type of sensor of the first vehicle into the latent representation in a common data space to minimize discrepancies between the first sensor data collected from the first type of sensor of the first vehicle and second sensor data collected from a second type of sensor of a second vehicle;
receiving, by the computing system of the first vehicle, the latent representation of the first sensor data from the server;
decoding, by the computing system of the first vehicle, the latent representation to generate map data corresponding to the first sensor data; and
associating, by the computing system of the first vehicle, the map data corresponding to the first sensor data with the existing HD map to generate an updated HD map.