The approach regularly has the networks compete against each other, with weaker examples being replaced by stronger “progeny” that are copies of the better-performing networks with slightly tweaked parameters (just as a child isn’t a perfect clone of its parent). This automatically gets rid of the poorer-performing networks while saving Waymo from having to retrain networks from scratch — they’ve already inherited know-how from their parents.

There is a risk that the method is focused too much on short-term improvements. To fight this, Waymo created “niches” where neural networks challenged each other in sub-groups to get strong results while preserving diversity that could be better-suited for real-world driving conditions.

The results were promising when applied to pedestrian detection. The PBT approach dropped false positives by 24 percent, even though it took half as much time. The experiment went so well that Waymo has even been using PBT across other models. That, in turn, promises self-driving cars that can better cope with the complexities of driving and avoid collisions.