Can you tell which half of the image is simulated data and which side is real sensor data?

Waymo announced a new simulation framework recently, both on its own blog and in a feature story with The Verge. The framework is called SimulationCity.

SimulationCity seems awfully reminiscent of CarCraft, the simulation engine that Waymo made famous in 2017. It’s been four years, which is certainly time for a refresh.

The Verge article is a little cagey about the distinction between SimulationCity and CarCraft:

“The company decided it needed a second simulation program after discovering “gaps” in its virtual testing capabilities, said Ben Frankel, senior product manager at the company. Those gaps included using simulation to validate new vehicle platforms, such as the Jaguar I-Pace electric SUV that Waymo has recently begun testing in California, and the company’s semi-trailer trucks outfitted with sensing hardware and the Waymo driver software.”

Waymo is using a new neural network they developed called SurfelGAN (“surface element generative adversarial network”) to better simulate sensor data, especially complex weather conditions like rain, snow, and fog.

Waymo’s blog post features several different videos and GIFs of SimulationCity, and each looks a little different. One video seems focused on behavioral planning, and features an animated Waymo semi-truck on a highway surrounded by moving green rectangular prisms that are meant to represent other vehicles on the road.

Another video seems to be simulating lidar point clouds.

And yet another video shows high-resolution simulated images paired side-by-side with real camera frames. It’s genuinely challenging to figure out which half of the image is simulated and which half is real.

All of that together seems to indicate that SimulationCity is a comprehensive simulation solution, more than a specialized solution for just camera images. I bet they can run perception, localization, prediction, planning, and maybe even control simulations within the framework, at varying speeds. Impressive.

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