Stacking shelves in a warehouse is one task. Driving is not one task. There are too many corner cases for a modern-day AI system to perform as well as a median driver in, say, 95% of environments and settings in North America and Europe. I think the argument is that such a system might as well be AGI.
The idea that an AI must have the ability to learn how to do anything in order to learn how to drive seems like an extremely pessimistic and misguided goalpost. That is also not how iterative development works.
I think ML is fantastic, and combined with LiDAR, inter-vehicle mesh networking, and geofenced areas where humans take over, we could quickly arrive at mostly automated driving without trying to reinvent the human brain. We should also be more focused on enforcing established legal limits to newly manufactured cars. Just preventing someone from exceeding the speed limit or driving the wrong way would start saving lives immediately. It would also allow traffic flow to be optimized, and eventually prioritize emergency traffic or allow metro areas to be evacuated efficiently for things like natural disasters.
It would be great to see the dawn of AGI, but I don't think it will ever happen with classical computation. GPT-3 spits out nonsense with the input of the largest and easiest to parse portion of reality, and I have not seen any ML approach replicate the abilities of something as simple as bacteria. ML requires constant validation from human operators, so the same is going to hold true for ML powered vehicle navigation.
