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AssigneeNVIDIA Corporation
InventorsAlexander A Popov, Alperen Degirmenci, Nikolai Smolyanskiy, David Ambrose Wehr, Alexey Kamenev, Ryan Oldja, David Per Zachris Nister, Ruchita Bhargava, Urs Andrew Muller
Estimating the term…
A probabilistic state simulation stack may be used to estimate and represent the state of a scene, including the state of an ego-machine (e.g., speed or position), traffic dynamics (e.g., the behavior of other road users), and/or static elements in the environment, a driving (or other navigation) policy may be co-trained as part of the probabilistic state simulation stack using a ground truth representation of human driving data, and at least a portion of the trained probabilistic state simulation stack may be deployed as an end-to-end drive stack in an autonomous or semi-autonomous machine (or some other type of control stack for other applications). This approach may be used to develop a robust driving policy by sampling state distributions predicted by the probabilistic state simulation stack to generate (e.g., simulate) any number of new (e.g., driving) situations and traffic scenarios and training the policy to handle these previously unseen scenarios.
One or more processors comprising processing circuitry to:
A method comprising:
A system comprising one or more processors to control, within a simulation that is rendered using one or more light transport simulation algorithms, one or more operations of a simulated ego-machine based at least on a scene embedding representing a simulated environment in the simulation, the scene embedding obtained based at least on applying a representation of a sequence of simulated sensor data generated using one or more simulated sensors of the simulated ego-machine to one or more first neural networks (NNs) comprising one or more encoders.
Dependencies are inferred from the claim text.