Abstract: In one embodiment, a method is provided. The method includes generating a set of candidate training data based on a simulated environment and a first set of environmental parameters. The method also includes training a machine learning model based on the set of candidate training data. The method further includes obtaining a set of segmentations based on the machine learning model and a set of test data. The method further includes determining whether a mean intersection-over-union (MIOU) of the set of segmentations has increased by more than a threshold change. The method further includes generating a next set of candidate training data based on the simulated environment and a second set of environmental parameters for the simulated environment, in response to determining that the MIOU has increased by more than the threshold MIOU amount.

Abstract: In one embodiment, a method is provided. The method includes receiving, at an input of a first machine learning model, first input data representing an environment. The method also includes determining, by the first machine learning model, a set of objects within the environment based on the first input data. The method further includes determining, by a second machine learning model, a set of behaviors for a second set of objects. An input of the second machine learning model is coupled to a set of intermediate layers of the first machine learning model. Determining the set of objects and determining the set of behaviors for the second set of objects is performed at least partially simultaneously.