The point is not "ML is bad and rules are good". This is a super hard engineering problem. In my view you need to use all techniques and technology to the fullest so that they complement each other if you want any kind of functional system with some amount of reasonable safety guarantees. My position is that in life-or-death-type of applications there are no magic shortcuts. You need to limit the ODD (primarily geo and speed) to be able to validate the system.
The benefits with rules is that they can be reviewed and understood. The benefits with ML is that it is likely, to some extent, to handle the cases for which you cannot write rules.
Rules are great for interpreting the actual traffic laws, whereas ML is great for interpreting behavior for example. Rules are great imho as a kind of general safety net around the ML.
There comes a time when you get to situations that extremely rare and machine learning cannot handle. To have a driverless deployment you need to carefully think through your stack to make sure that even if machine learning does not solve everything 100%, your full self-driving product does.
If you are to release a driverless service you need to also answer the question of how how to make sure the whole thing is fully robust and I believe that adds a level of complexity that Tesla is yet to tackle.
It's impossible to regression test "the world" for every point release that might cause horrible accidents because of some weird bug. So I don't see wide-ODD happening anytime soon regardless of approach.
