The comparison to animal intelligence is apt.
Animals are very good at responding to specific situations with fixed action patterns.
Humans are very good at adapting to new situations by training new action patterns.
Self driving cars tread far more in the territory of machine learning than AI. AI involves ML, but ML doesn't necessarily involve proper AI. ML is essentially creating a framework of existing data that the computer can apply to similar situations. This is why you hear terminology like "training a dataset" in ML -- you are just telling it to act upon new situations in as similar of a capacity as possible to previous situations.
AI to me is a very different thing, in that it doesn't require structured inputs and outputs. Even as chaotic as autonomous driving is, it's still a structured system that takes inputs about road rules and surrounding objects and aligns the car with an outcome where it follows road rules and doesn't hit anything.
True AI is a machine that can reason for itself in unstructured situations, which would likely involve ML that is very good at making inferences about how existing data applies to tangentially related situations. I'm sure at some point there will be a distinction between AI that aims to create outcomes as similar as possible to human decision making (perhaps it could be trained by you to make decisions the way you would), and AI that aims to emulate human thought process literally down to the neurotransmitter level.