NVIDIA’s Neda Cvijetic Explains the Science Behind Self-Driving Cars

by Lauren Finkle

What John Madden was to pro football, Neda Cvijetic is to autonomous vehicles. No one’s better at explaining the action, in real time, than Cvijetic.

Cvijetic, senior manager of autonomous vehicles at NVIDIA, drives our NVIDIA DRIVE Labs series of videos and blogs breaking down the science behind autonomous vehicles.

A Serbian-American electrical engineer, Cvijetic seems destined for this role. She literally grew up in the shadow of Nikola Tesla. His statue in Belgrade stood across the street from her childhood home.

On this week’s AI Podcast, Cvijetic spoke to host Rick Merritt about what’s driving autonomous vehicles. She also shared her perspective on how both broad initiatives and day-to-day actions can promote diversity in AI.

 Key Points From This Episode:

  • Autonomous vehicles use three key techniques: perception, localization, and control and planning.
  • Each self-driving car runs on dozens of deep neural networks, which are each trained on thousands of hours of real-world driving data and on NVIDIA DRIVE Constellation, which provides extensive testing in virtual reality before the car even hits the road.
  • Autonomous vehicles drive safely because diversity and redundancy are designed into their systems. Multiple cameras with overlapping fields of view, radar, and more provide a wealth of perception data for the highest level of accuracy.


“I want every driver out there to feel that they understand AI, to understand how AI works in self-driving cars, and feel empowered by that understanding” — Neda Cvijetic [1:56]

“The NVIDIA DRIVE simulator seeks to create some of these corner cases that might take years to actually observe” — Neda Cvijetic [10:36]

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