Comment by boulos

(Disclosure: I work for Waymo)

While there is plenty of classical robotics code in our planner, I wouldn't want people to assume that we don't use neural networks for planning.

Just because we don't deploy end-to-end models (e.g., sensors to controls), but have separate perception and planning components doesn't mean there isn't ML in each part. Having the components separate means we can train and update each individually, test them individually, inject overrides as needed, and so on. On the flip side, it's true that because it's not learned end-to-end today that there might exist a vastly simpler or higher quality system.

So we do a lot of research in this area, like EMMA (https://waymo.com/research/emma/) but don't assume that our planning isn't heavily ML based. A lot of our progress in the last couple of years has been driven by increasing the amount of ML used for planning, especially for behavior prediction (e.g., https://waymo.com/research/wayformer/)