Training a variable autoencoder using a diffusion model

Abstract

Techniques for training a variable autoencoder to output data associated with one or more objects in an environment are described herein. For example, the techniques can include training an encoder and a decoder of the variable autoencoder to improve predictions over time. The variable autoencoder can be trained to output object occupancy information (e.g., a bounding box, heatmap, feature vector) and/or object attribute information (e.g., an object state, an object type, etc.). A vehicle computing device can use an output from a trained autoencoder during vehicle planning, which may include simulation.