> using that argument we could dismiss any of the other technologies ignoring the fact that more (and different) data typically allows you to make better decisions.
Bellman gave us the bellman equations, but also gave us the term curse of dimensionality. The equations he shared and the modeling problems he encountered are fundamentally related to modern reinforcement learning. More data doesn't come without cost. So often I hear people speak of the introduction of lower resolution as equivalent to the introduction of error, but this is a false equivocation. Latency in decision making means the introduction of a lower resolution can increase the resolution error, but still decrease the solution error. This is so fundamental a result that it applies even to games which don't have latency in their definition. Consider poker. The game tree is too big. Operating with respect to it as an agent is a mistake. You need to create a blueprint abstraction. That abstraction applied to the game introduces an error. It is lower resolution view of the game and in some ways it is wrong. Yet if two programs try to compete with each other, the one that calculated with respect to the lower resolution version of the game will do better than the one that did its calculations with respect to the higher resolution view of the game. High resolution was worse. The resolution without error was worse. Yet the game under consideration was orders of magnitude simpler than the game of a self driving car.
I've been paying some attention to this debate and I'm not convinced yet that the situations under which LiDAR is superior are sufficient. I think we agree on that already. For me, this reduces the set of situations under which LiDAR is able to be considered superior - if vision is bad, but you need vision, then better to avoid the situation then use the wrong thing [1]. So the situations under which LiDAR becomes superior becomes a subset of the situations that it is actually superior. That subset doesn't seem very large to me, because both LiDAR + vision and vision alone are both necessarily going to be reducing the dimensionality of the data so that the computation becomes more tractable.
[1]: This isn't exactly uncommon as an optimization choice. It'll get dark later and you'll stop operating for a time. Then light will come. You'll resume operation. This is true across most of the species on this planet. If you are trying to avoid death by car accident you could do worse than striving to operate in situations where your sensors will serve you well.