[BugsJustFindMe]

> there probably ought to be a layer on top of that that's interpreting the ML output that can take the decision to pick one

It needs to be an input, not just a filter on output. A heuristic that doesn't account for the previously observed state is insufficient because prior existence information changes what gets detected. And it needs to lean heavily toward "if we saw something there before, the safest course of action is to assume that it's still there until strongly indicated otherwise".

[Doxin]

It can be a filter on the output and account for previously observed state. E.g. one trivial (and probably pretty bad) solution in that direction would be to boost the probability of small state changes as opposed to large changes.

> if we saw something there before, the safest course of action is to assume that it's still there until strongly indicated otherwise

Normally I'd agree with you, but driving a car is a bit of a special case since in may cases stomping on the brakes is way worse than doing nothing. Take the video of the tesla seeing spurious traffic lights[0] for instance, should it really do an emergency stop in the middle of traffic in that case?

All the machine learning can do is give you probabilities on what the state of the world around the car might be. The software needs to pick from those probabilities some state to act on in such a way to be the least likely to endanger anyone. There isn't a simple correct answer. all the variables need to be weighed. currently teslas seem to ignore some pretty important variables while driving.

[0] https://twitter.com/sascha_p/status/1400173874285744129