Tesla's BEVnet that predicts roadway geometry from vision is essentially a global HD map in a compressed format. At least when it's fully trained and labeled from global roads. Instead of storing explicit, human-understandable maps, they are encoded as weights in a network, and the maps are unpacked out of the network when you input camera frames instead of GPS coordinates. This has the benefit of working entirely offline, doesn't require precise localization (or any localization), and it doesn't totally fall apart when it encounters an unmapped roadway or stale map data.What's becoming evident is that HD maps are leading to a local maximum. It's been how many years since they were first used?
HD maps are very tempting to use, for the very reasons we see 10.2 fail. But, Elon and everyone keeps repeating the same thing, which is that vision needs to be "solved" before FSD can be achieved. Basically, vision is the rate-limiting factor for FSD development, not HD maps. Once vision is adequate for multiples of human safety, then HD maps aren't useful, they'd just be more noise.