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I think car companies try to rely way too much on machine learning. You get some promising results fast, but it is all inside a black box, both verifying the correctness and changing what is wrong are almost impossible jobs.

Maybe machine learning can be used to tell the difference between a dog and a plastic bag, but you'll need some hard code to describe how to react to either.

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>ut you'll need some hard code to describe how to react to either

My understanding is that's largely how it's done. The ML part is mostly about recognizing objects. But the car doesn't "learn" how to drive. It's told how to drive depending on what's happening in its field of view.

Which is why there's probably misperceptions about the importance of miles on the road. It uncovers un-programmed situations but it's not like the car runs over someone and reinforcement learning leads to it not doing that next time.

There are some levels in between, like feeding an ML algorithm recordings of human drivers and expecting it to learn how to drive from watching those.

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