VectorNet aims to help predict the movements of road users by building representations to encode information from maps, including real-time trajectories. Waymo, like rivals Cruise and Aurora, collects high-definition, precise-to-the-centimeter maps of regions where its autonomous vehicles drive. Paired with sensor data, these provide context to the Waymo Driver, Waymo’s full-stack driverless system. But the maps can’t be incorporated into prediction models until they’ve been rendered as images and encoded with scene information, like traffic signs, lanes, and round boundaries.

That’s where VectorNet comes in. Unlike the convolutional neural networks it replaced, which operated on computationally expensive pixel renderings of maps, VectorNet ingests each map and sensor input in the form of vectors (sketches made up of points, lines, and curves based on mathematical equations).
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