A method learns unsupervised world models for autonomous driving via discrete diffusion. The method includes encoding an observation of an actor for a geographic region using an encoder to generate a prior frame of prior tokens. The method further includes processing the prior frame with a spatio-temporal transformer to generate a predicted frame of predicted tokens. The spatio-temporal transformer includes a spatial transformer and a temporal transformer. The method further includes processing the predicted frame to generate a predicted action for the actor. The method further includes decoding the predicted frame to generate a predicted observation of the geographic region.
LEARNING UNSUPERVISED WORLD MODELS FOR AUTONOMOUS DRIVING VIA DISCRETE DIFFUSION
2025-03-27
Patent
Electronic Resource
English
Transition to unsupervised autonomous driving mode of ADS
European Patent Office | 2023
|TRANSITIONING TO AN UNSUPERVISED AUTONOMOUS DRIVING MODE OF AN ADS
European Patent Office | 2023
|