US 12,434,739 B2
Latent variable determination by a diffusion model
Ethan Miller Pronovost, Redwood City, CA (US)
Assigned to Zoox, Inc., Foster City, CA (US)
Filed by Zoox, Inc., Foster City, CA (US)
Filed on Dec. 22, 2022, as Appl. No. 18/087,540.
Application 18/087,540 is a continuation in part of application No. 17/885,671, filed on Jun. 30, 2022, granted, now 12,311,972.
Application 18/087,540 is a continuation in part of application No. 17/855,696, filed on Jun. 30, 2022, granted, now 12,217,515.
Prior Publication US 2024/0101157 A1, Mar. 28, 2024
Int. Cl. B60W 60/00 (2020.01); B60W 30/09 (2012.01); B60W 30/095 (2012.01); B60W 40/04 (2006.01); B60W 50/00 (2006.01); G06N 3/04 (2023.01); G06N 20/00 (2019.01)
CPC B60W 60/0027 (2020.02) [B60W 40/04 (2013.01); G06N 3/04 (2013.01); B60W 2554/404 (2020.02); B60W 2554/4046 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a diffusion model, map data representing an environment, wherein the diffusion model comprises one or more self-attention layers;
receiving, by the diffusion model, condition data representing a state or an action of an object in the environment;
determining, by the diffusion model and based at least in part on the map data, the condition data, and output data associated with the one or more self-attention layers, latent variable data associated with the object;
inputting the latent variable data into a machine learned model;
determining, by the machine learned model and based at least in part on the latent variable data, an object trajectory for the object to follow in the environment; and
causing control of a vehicle in the environment by executing one or more vehicle maneuvers based at least in part on the object trajectory to avoid potential interaction between the vehicle and the object.