As someone with a lot of experience in self-driving cars, my opinion has changed over the course of the last decade from "we can create smart enough models of these separate problems to create a statistically safer product" to "first you need to invent general AI."
It becomes immediately obvious as you encounter more and more edge cases, but you would never even begin to think of these edge cases and you have no idea how to handle them (even when hard coding them on individual cases - and you couldn't anyway as there are too many) so you realize the car actually has to be able to think. What's worse, it has to be able to think far enough into the future to anticipate anything that could end badly.
The most interesting part of self-driving cars is definitely on the prediction teams - their job is to predict the world however many seconds into the future and incorporate that into the path planning. As you can guess, the car often predicts the future incorrectly. It's just a ridiculously hard problem. I think the current toolbox of ML is just woefully, completely, entirely inadequate to tackle this monster.
A model of that kind has to be able to generate predictions given the agents interaction with the object. Ie., how will the thermostat behave if I turn on the air conditioning.
The problem is that we acquire these models during an entire lifetime of being embedded in an environment, esp. a social one.
A self-driving car is never going to predict human behaviour given its own behaviour without having lived a human life. The question of what it should do given how another driver/pedestrian/etc. behaves isn't something it can solve with "mere statistics".
It might be that human behaviour around roads ends up being extremely predictable, I'm doubtful that's the case. I think a car is going to need a Theory-of-Mind to navigate complex social driving environments, ie., a causal model of the behaviour of other animals.