Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

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.

Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.

Abstract: A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.