However, the data side of the equation is still underdeveloped due to the limitations of real-world data, which is incomplete and time consuming and costly to collect. Engineers have dedicated significant time to refining algorithms. The deep neural networks powering an autonomous vehicle’s perception are composed of two parts: an algorithmic model, and the data used to train that model. For the NVIDIA DRIVE team, synthetic data has been an effective, critical component of its AV development workflow. Data generated by DRIVE Sim is used to train deep neural networks that make up the perception systems in autonomous vehicles.
In a demo, Huang showed the power of Omniverse Replicator when applied to autonomous vehicle development using DRIVE Sim.ĭRIVE Sim is a simulation tool built on Omniverse that takes advantage of the many capabilities of the platform. In his keynote at GTC, NVIDIA founder and CEO Jensen Huang announced NVIDIA Omniverse Replicator, an engine for generating synthetic data with ground truth for training AI networks. The gap between reality and simulation just became narrower.
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