Why is FSD proving to be so complicated in dynamic environments I.e. rain, low light, lack of road markings etc. is it an algorithm/computation limitation, is it sensory hardware, is it both? Or is it accomplishable, but not profitable?
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It’s a decision tree problem, mostly.
Basically any task can be written as a decision tree – what should you do at any juncture, with a result.
In reality, most tasks aren’t written like this, because the amount of possible variables quickly dwarfs the ability to write the tree.
Now consider FSD. Even at a basic level. It needs to determine what road it’s on, merely to know what speed it’s allowed to drive – some of that comes from GPS, but anyone that’s used that knows that it can get confused about what road you are on at times. So even before you’ve done anything, it has to marry up GPS and a map, to get an idea of where it is and how fast it’s allowed to go. And that’s the easy logic.
Everything else is about the decision tree and conflict resolution – what should I be doing and what should I be doing when the information I have conflicts with something else I know. This latter is why Tesla removed the radar guidance from their cars – the radar picked up bridges across roads and would slow down as the conflict suggested an obstruction.
And these are very basic examples. What should it do at a 4 way stop sign? What size of object entering the road should trigger stopping/swerving etc (this is the one that Tesla doesn’t like as their cars have had a tendency to ignore children as an obstacle).
The scale of the complexity is astonishing. Humans driving make enormous numbers of calculations and considerations subconsciously, so even to program the cars systems, as a human you have to work out what you do subconsciously and then work out how to make a computer do that.
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