You get it correctly. Deep learning isn't inherently good for cases where you need to be right 100% of the time. But we don't have a better tool so the industry

You get it correctly. Deep learning isn't inherently good for cases where you need to be right 100% of the time. But we don't have a better tool so the industry is trying to see how far they can push it. One big open question in self driving is: Do you have to be able to see/classify everything in the scene all the time to be a good driver? I think the answer is no (see: humans.) But, because most self driving stacks in the industry have chosen "3d classified representation of all of the objects in the scene" as a fundamental interface between perception and planning stages, there is a big challenge. (e.g. Representing uncertainty usefully is exceptionally difficult with this interface, among other things.)