When you limit your service area, it becomes feasible to train your models on pretty much (or literally) every road in the service area. Every single permutation of weird intersection it will ever come across. If your L4 car only ever has to drive in San Francisco, it doesn't matter that this ML model would have no hope when introduced to Salt Lake City. The model never has to spend a moment excluding any of the millions of potential things it has never been trained on and will never see.

Both the ease of data collection and the absence of contextual noise makes L4 machine learning an order of magnitude easier.

Can you point me towards any recent FSD Beta video where there was an important failure of object detection?