Invention Application
- Patent Title: TRAINING, TESTING, AND VERIFYING AUTONOMOUS MACHINES USING SIMULATED ENVIRONMENTS
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Application No.: US16366875Application Date: 2019-03-27
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Publication No.: US20190303759A1Publication Date: 2019-10-03
- Inventor: Clement Farabet , John Zedlewski , Zachary Taylor , Greg Heinrich , Claire Delaunay , Mark Daly , Matthew Campbell , Curtis Beeson , Gary Hicok , Michael Cox , Rev Lebaredian , Tony Tamasi , David Auld
- Applicant: NVIDIA Corporation
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F9/455 ; G06N20/00

Abstract:
In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.
Public/Granted literature
- US11436484B2 Training, testing, and verifying autonomous machines using simulated environments Public/Granted day:2022-09-06
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