Our vision and persistent world model are good, but all humans are overconfident in their ability to drive in poor conditions (or even normal conditions, for many people.)

A computer isn't going to look down at a text message on their phone, fall asleep at the wheel, or get in a car while intoxicated. All which occur literally every day. People take driving for granted, because (at least in the US) we have to. Our sprawling suburbs couldn't function without it, so the barrier to entry is simply being a teenager.

Would I completely trust Tesla's auto-driving? No, not at all. But I at least trust them as much as I do an average driver, which is still just barely. The difference with Tesla's system is that one failure leads to improvement of the entire fleet, whereas one human failure maybe improves that one human driver. That means self-driving cars will continuously improve, while humans will just continue to be terrible drivers.

Robustness is a key property of the methods and models used in self driving cars.

For a human, the noise isn't bad recognition, it's corrupted sensor data via distraction. It's one of the most common causes of accidents. For the AI agent an elephant will never appear unless it's been trained to be recognized. More likely it's recognized as an obstacle, which is ok. But keep in mind that years of Google's autonomous vehicle success make this pretty improbable, certainly less than in the average driver.

Much of the fear here is irrational and natural when humans lose control.

a) well, no. we only have about 100 years dealing with these problems in context of driving cars and we're really bad at it; b) that seems like shifting goalposts, what we care about is whether driver's picture of the world is correct and actions are sensible with respect to that picture, it just so happens that driverless cars' picture is visual (or close to visual), but if you're judging them on entirety of input and side-effects it's only fair to judge our brains by the same standard.

Some adversarial methods work on black-box networks - has any work been done on applying those to human vision? We know there are a wide range of simple inputs that produce unexpected results (eg. optical illusions​).