Fanboy or not, we don't know how much Waymo's model relies on an army of contractors labeling every stop light, sign, and trash can that, sure, they're using LIDAR to detect them and not cameras. We also don't know much about Tesla's Robotaxi initiative and how much human help they're relying on either.
first of all, their approach does not rely on map level labeling for runtime. They do that for training, but so does every other player. High precision maps are used as a form of GPS in their solution. They also use it to determine delta if road conditions change and alert ops.
Second of all, they’re using cameras to detect things like signs and trash cans! I don’t know where this misconception came from that Waymo only uses lidar, but it demonstrates a lack of familiarity with the problem space and the solutions
i cant edit this but it's bugging me "does not rely on map level labeling for runtime" isn't quite true. they do use tagging in their centralized mapping that the waymo driver uses as one source of truth. but ultimately, the onboard sensors and the onboard classifier and pathing engine are used.
this allows them to tag things that their cars are not recognizing, but then are fed back into the training set and validated. it allows them to "live patch" troublesome sections of road but it's not intended for long term use.
they also use this to close sections of road, such as for car accidents, flooding, etc.
From my previous comment, in case you didn't see it
> Again Waymo's lead suggests this logic might be wrong but I think there is a solid engineering defense for moving towards just computer vision. Cameras are by far the best sensor, and there are tangible benefits other than just cost.
> solid engineering defense for moving towards just computer vision
COUNTERPOINT: WAYMO