Considering that Elon Musk says we'll have full self-diving cars (from an engineering standpoint) in 2 or 3 years, this estimate seems very pessimistic.
Somewhere in the middle there's probably a point where self driving is demonstrably safer than human, even before they're "ready" for everywhere.
It's just a question of more training(altough a recent NHTSA? regulation says the planning stage cannot use black box techniques like DNNs).
And once self-driving is a business, i suspect we'll find ways to get said training data for cheap enough and fast enough. For example Tesla/Uber drivers as trainers .
The way I see it (as a software engineer myself), is that each wreck that the AP did not prevent gets added as a test case in the AP unit tests that all have to pass. Also, each time a human wrecks, it could/will send the data to Tesla to continue simulations and ensure AP would not wreck in the same conditions.
How many of the wrecks, even fatal ones, essentially a different driver making the same mistake over and over? Likely millions. Real fleet learning would simply solve that problem once, and prevent thousands of fatalities as a result. As more and more AP capable cars hit the road, this simply will exponentially increase. Thirty years is laughable seeing how much computing has improved in merely the past 20. Maybe 5 tops assuming the legal hurdles are actually surmountable.
http://www.theverge.com/2016/10/19/13341194/tesla-autopilot-...
They also have every single human driver wreck captured with AP capable hardware to make fully autonomous vehicles safer. The way they are doing it right now is brilliant.
Edit: As Elon has previously said, not releasing something that saves lives is actually unethical. If a single life is saved by AP software that would have died if the vehicle was driven by human meatware, not releasing it is the unethical thing.
Elon Musk's predictions are consistently unreliable.