Obviously, only vision and hearing are required for driving. The question isn't "can it work," the question is "which approach will be economically viable on what time scale, and is there a winner-takes-all dynamic that excludes the approach that comes second."
Humans don't have wheels either. Is Tesla planning to replace wheels in their cars with legs, Flintstones style?
This isn't a question of max-sensors vs min-sensors, though. It's a question of max-sensors vs max-data under economics constraints that forbade "why not both?". Tesla chose max-data, and they now have a 40x data advantage thanks to their choice, and probably more once you consider the improved diversity of data collected from private cars in every corner of the country and world.
If you were training a self-driving car, would you rather have 40x the data or would you rather have point clouds that are only a smidgen better than what you can get from modern monocular depth reconstruction on the vision data?
It's proof that the problem can be solved with vision when your driver is a human. That doesn't necessarily prove it for non-human drivers implemented on currently known AI architectures.
Humans can learn from a lot less data then current non-human AIs require. We also learn in the field, often recognizing when we've made a mistake and consciously updating ourselves to handle similar situations better in the future. It is possible that this is necessary to make vision only driving work well.
I think you dodged the question because you know that 40x data is the clear winner.
I responded to the part of your comment that interested me, which concerned what we can infer from the ability of humans to do vision based driving.