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It's not that deep learning is intrinsically incapable of driving -- it's that the situation space is extremely high-dimensional (due to edge cases), and that a deep learning system requires to be trained on a *dense sampling* of the same space that the system will operate in.
Because such a representative, dense sampling is impossible to obtain, even when heavily leveraging simulated environments, the symbolic approach will prevail (specifically, an approach that is mostly symbolic but blends human abstractions with learned perceptual primitives)
human brain does not learn its structure/connectivity from scratch - it leverages genetic "priors"
I can’t think of a safety critical system built from ML (programmed from examples). In part there isn’t a clear process to certify the system performs as specified. Safety critical systems told HOW to perform and verification and validation is a major cost.
There are other challenges such as the system recognizing when its environment strays from its training.
Ultimately, the important question is how to learn the relationship to human symbols and abstractions and synthesize it into our learning process.
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