I'm not in the market for a new car, but from what I read: There is something called "city safety" by Volvo, and I know that Mercedes has the similar tech (a friend learned that by not being run over by a distracted driver). So there are already technologies to prevent (or at least reduce the severity) of what happened to you (assuming he was below a certain speed threshold).

In constrast to the whole self driving stuff this DL is popular for: User input overrides DL input.

There is no evidence that deep learning would give better performance than other collision avoidance algorithms in such a scenario.

But that doesn't mean we shouldn't try. I was agreeing with GP (u/amelius) because I had the same idea when reading the post, but the parent of your comment (u/simonsarris) makes a good point: we might not know deep learning as well as we might like to know it, given that it is being used in applications that have the potential to kill, but we also don't know our own brains that well.

Even if we don't understand deep learning to the degree that we would like, we can observe its safety record and compare it to humans'.

There's a difference between active and passive use of self-driving with the current technology.

Passive self-driving systems that take over when the human gets distracted/unwell are great because human vision exceeds computers where as computers are always alert. This would capture the case you describe, I think it would also have a massive improvement for when bus/lorry drivers should collapse at the wheel (Elon Musk used this as a valid use case for Tesla auto-pilot in the Tesla Semi unveiling).

However active self-driving systems (e.g. Tesla's auto-pilot) are currently worse because they rely on computer vision and humans to be always alert.