Nobody said anything about 15 feet but you. If you're pulling over in a normal heavy downpour with 500 ft of visibility, you're less safe than the people who continue to drive. Being pulled over to the side of any high-speed road is an unsafe condition that should be avoided.

Self-driving cars would be putting lives at risk if they just pulled over and shutdown on every summer thunderstorm.

With 500 ft of visibility, the auto-steering/auto-propelled car shouldn't have any trouble either.

How does a car know it can't see well?

It uses its sensors. If the LIDAR shows only white noise it knows that it can't see well.

White noise certainly isn't the only indicator that one can't see well.

How does a human know it can't see well?

Well, that's a great question. It's also basically equivalent to the one that I asked. My point would be that I don't have an answer to that question, and given your response, I bet you don't either. Which leaves either of us at a loss when we try to create a safe and reliable autonomous vehicle.

It's certainly not an unanswerable question. It's just quite hard to verify that they answer you have is a sufficiently correct one.

> It's just quite hard to verify that they answer you have is a sufficiently correct one.

Why? It seems to me that estimating the confidence of a prediction by the quality of measurements is not an infrequent machine learning task. It'd be more complex for a driving algorithm than calculating the confidence interval of a linear regression, but it's in the same theme.

A computer can predict with 100% confidence if you give it few enough measurements. That doesn't mean it will make the correct decisions. You have to already know you're measuring the right things.

If you don't want it to do that, don't program it that way. Code it to assume some and to estimate the degree of mismeasurement.

Fair enough! You're absolutely right: I don't know the answer. I misinterpreted your post as suggesting/implying that there is no answer for a computer.