US 11,670,088 B2
Vehicle neural network localization
Mokshith Voodarla, Santa Clara, CA (US); Shubham Shrivastava, Sunnyvale, CA (US); and Punarjay Chakravarty, Campbell, CA (US)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Dec. 7, 2020, as Appl. No. 17/113,171.
Prior Publication US 2022/0180106 A1, Jun. 9, 2022
Int. Cl. G06V 20/56 (2022.01); H04W 4/46 (2018.01); G06N 3/08 (2023.01); G06V 10/25 (2022.01); G06V 10/75 (2022.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06V 10/82 (2022.01); G06N 3/088 (2023.01); G06N 3/047 (2023.01)
CPC G06V 20/56 (2022.01) [G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/25 (2022.01); G06V 10/751 (2022.01); G06V 10/82 (2022.01); H04W 4/46 (2018.02); G06N 3/047 (2023.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising a computer including a processor and a memory, the memory storing instructions executable by the processor to:
receive a plurality of temporally successive vehicle sensor images as input to a variational autoencoder neural network that outputs an averaged semantic birds-eye view image that includes respective pixels determined by averaging semantic class values of corresponding pixels in respective images in the plurality of temporally successive vehicle sensor images;
from a plurality of topological nodes that each specify respective real-world locations, determine a topological node closest to a vehicle, and a three degree-of-freedom pose for the vehicle relative to the topological node closest to the vehicle, based on the averaged semantic birds-eye view image; and
determine a real-world three degree-of-freedom pose for the vehicle by combining the three degree-of-freedom pose for the vehicle relative to the topological node and a real-world location of the topological node closest to the vehicle.