Abstract: In various examples, a plurality of poses corresponding to one or more configuration parameters within an environment—such as a location of a machine within an environment, an orientation of a machine within an environment, a sensor angle pose of a machine, or a sensor location of a machine—may be used to generate training data and corresponding ground truth data for training a machine learning model—such as a deep neural network (DNN). As a result, the machine learning model, once deployed, may more accurately compute one or more outputs—such as outputs representative of lane boundaries, trajectories for an autonomous machine, etc.—agnostic to machine and/or sensor poses of the machine within which the machine learning model is deployed.

Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.

Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.