That's my biggest problem with AI and neural networks. You can't really measure progress here. If you wanted the same safety standards as for every other automotive software you'd have to test drive for hundreds of thousands of kilometres after every change of parameters, because there's no way to know what has changed about the AI's behavior except for testing it thoroughly.
Compare this to classic engineering where you know the changes you've made, so you can rerun your unit tests, rerun your integration tests, check your change in the vehicle and be reasonably sure that what you changed is actually what you wanted.
The other approach to autonomous driving is to slowly and progressively engineer more and more autonomous systems where you can be reasonably sure to not have regressions. Or at least to contain your neural networks to very very specific tasks (object recognition, which they're good at), where you can always add more to your test data to be reasonably sure you don't have a regression.
I don't think we'll see too many cars being controlled by neural networks entirely, unless there's some huge advancement here. Most of the reason we see more neural networks now is that our computing power has reached the ability to train sufficiently complex NNs for useful tasks. Not because the math behind it advanced that much since the 60s.
Compare this to classic engineering where you know the changes you've made, so you can rerun your unit tests, rerun your integration tests, check your change in the vehicle and be reasonably sure that what you changed is actually what you wanted.
The other approach to autonomous driving is to slowly and progressively engineer more and more autonomous systems where you can be reasonably sure to not have regressions. Or at least to contain your neural networks to very very specific tasks (object recognition, which they're good at), where you can always add more to your test data to be reasonably sure you don't have a regression.
I don't think we'll see too many cars being controlled by neural networks entirely, unless there's some huge advancement here. Most of the reason we see more neural networks now is that our computing power has reached the ability to train sufficiently complex NNs for useful tasks. Not because the math behind it advanced that much since the 60s.