Aurora Operations, Inc.

Simon Box

John Michael Wyrwas

Jessica Elizabeth Smith

Steven Keith Capell

Clinton Wade Liddick

Validating a simulation scenario for use in training a machine learning model for an autonomous vehicle includes determining a simulation scenario; executing a simulation based on a simulation scenario; monitoring execution of the simulation and receiving messages from the execution of the simulation; determining, by a first simulation monitor, whether the messages satisfy a first incident; and validating the simulation scenario responsive to the messages satisfying the first incident to produce validated simulation data. A system and method may also include determining, by a first simulation validator, whether the messages satisfy a condition; and validating the simulation scenario responsive to the messages satisfying the condition to produce validated simulation data.

Validating autonomous vehicle simulation scenarios

Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Generating autonomous vehicle simulation data from logged data

Simulation data of the autonomous vehicle is processed by executing a simulation based on the simulation data to generate a simulation result. Then a perception scenario is generated from the simulation result. The generated perception scenario is validated by verifying whether a constraint is satisfied to produce a validated perception scenario. The validated perception scenario can be used to create or refine a perception model used for controlling the operation of autonomous vehicles.

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Simon Box