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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FSD basically combines all the properties that computers are generally terrible at.
Computers & algorithms are deterministic and do best with very clearly defined inputs & outputs. For systems that rely on interaction with the outside world, that means precisely defined/controlled/known conditions. Driving features…the complete opposite.
Driving takes place in a wildly varying dynamic environment the car doesn’t control, with an unknown number/type/size/color of stationary & moving objects, none of which can be counted on to obey any accurate predictive model of behavior, in an extremely wide variety of environmental conditions, with operating rules/laws that change, may not be known, and are context dependent.
Doing this well requires really good pattern recognition, really good and fast heuristics, incredibly high dynamic range sensors, a lot of non-deterministic analysis, snap judgement, improvisation, and “driving empathy” (attempting to correctly guess the action of other entities in the environment based on real-time context). These are all skills that humans are really good at, thanks to billions of years of evolution trying to keep us alive in exactly this kind of environment, and computers are generally terrible at because they *don’t* have good deterministic algorithms to solve these problems. We can barely teach computers to accurately gauge human facial expressions or beat us at Go, which is trivial compared to the amount of environmental modelling a self-driving car needs to do in real time.
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