When we design classical control systems, the performance limitations are well understood. If we do not exceed the limits, we expect the system to be well beha

When we design classical control systems, the performance limitations are well understood. If we do not exceed the limits, we expect the system to be well behaved. By contrast, DNN/CNN based systems can be a bit of a black box. We can only evaluate performance empirically, not analytically. It is difficult to know where in the input space the failure modes lie. It is difficult to then build the larger system around it, because you do not know were the keep-out zones are. I think a study of the failure modes of CNNs shouldn't be interpreted as an all-or-nothing evaluation of the technology as a whole, but rather a step towards gaining some confidence regarding its reliability. A lot more work needs to be done before I will trust it to drive my car. Regarding the use of CNN's for autonomous driving, I think it is insane that people are trying to do this by trying to solve a VERY hard problem, i.e. making a machine that can do what the human brain does. Your neural net does not have enough labels to account for all possible scenarios. Instead, it would make more sense to redesign the infrastructure in a way that bounds the problem space. The current system is designed for human drivers. We should make a system that is easy to interpret for both human and machine drivers. Of course this infrastructure would benefit all car makers, not just the first mover.