Waymo says it’s beginning to leverage AI to generate camera images for simulation by using sensor data collected by its self-driving vehicles. A recent paper coauthored by company researchers including principal scientist Dragomir Anguelov describes the technique, SurfelGAN, which uses texture-mapped surface elements to reconstruct scenes and camera viewpoints for positions and orientations.
Autonomous vehicle companies like Waymo use simulation environments to train, test, and validate their systems before those systems are deployed to real-world cars. There are countless ways to design simulators, including simulating mid-level object representations, but basic simulators omit cues critical for scene understanding, like pedestrian gestures and blinking lights. As for more complex simulators like Waymo’s CarCraft, they’re computationally demanding, because they attempt to model materials highly accurately to ensure sensors like lidars and radars behave realistically.
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