The flickering is concerning. Kind of suggests their NN hasn’t learned basic object persistence yet.
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".
> 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.
Second, the cost of deciding that you're in the wrong lane is very different from the cost of deciding that there isn't an obstacle in front of you. The worst thing that happens when a navigator gets your position wrong is you miss a turn and spend a few extra minutes in the car. The worst thing that happens in the other case is someone dies instantly in a cloud of blood.
...and driving straight into barriers.
> the system doesn't know which input to trust
Well, clearly their camera network is untrustworthy, so...