Your eyes run on more-or-less fixed-function neural networks to see and recognize stuff.
The part of your brain that does the actual driving is much more symbolic in how it analyzes and reacts.
When you look at self-driving, there's a general pattern that it handles the 80% of easy stuff (the stuff that you could drive while texting and almost completely ignoring the road). There's a steep gradient down to "complete failure" from there. Accidents happen during a tiny, tiny subset of situations (a minuscule fraction of a percent).
Vehicle deaths are in the realm of 1-2 per 100 million miles driven (even lower total accidents if you factor in multiple deaths in one vehicle).
If you have a vehicle drive an average of 50mph for 8 hours every day (a very high average if not exclusively driving highways), it would take 700 years for that car to statistically have a fatal accident. For you to test an AI with a decent 95% confidence interval, you'd need hundreds of thousands of cars driving for years just to test a single model.
The "enough computation" or "enough data" idea simply isn't going to work here.
By the time humans have been proven superior there is a real chance that the AIs will have improved to be superhuman. The experience in gaming is that state of the art goes from ok vs amateurs to superhuman quite quickly.
Besides, non-fatal ACCIDENTS per mile are much more common, and we should be able to determine if those are reducing a lot faster.
With ML there is at least a path to solve way-easier-than AGI problems like maybe (semi?) autonomous cars, given enough compute and redundancy. With anything more old-school? Maybe you'd get lane detection that works on sunny days. And yeah you will be able to "debug" and prove things more easily but what's the point if there's no real way to fix the bugs if they are inherent to the flaws of symbolic approaches? If you think Ml has a problem with edge cases (and it does), keep in mind that GOFAI methods are usually not even close to be complete enough to have "edges". "the last 1% is the hardest to deal with" becomes more of a "if we can get over 60% it's a miracle"
Also, no matter how suboptimal the tesla autopilot is, I have not seen any figure that show that they do worse than humans w.r.t to fatality or accident rates. Maybe they are worse! But just the fact that they are even remotely close is not something that could've been possible even 15 years ago.
But I'd be thrilled if we get to anything better than current DL methods. Hell, it might even pull me back into the field! I just really doubt it's going to be old school symbolic or causal methods that will get us to the next big steps.