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
27.03.2025
Patent
Elektronische Ressource
Englisch
Transition to unsupervised autonomous driving mode of ADS
Europäisches Patentamt | 2023
|TRANSITIONING TO AN UNSUPERVISED AUTONOMOUS DRIVING MODE OF AN ADS
Europäisches Patentamt | 2023
|