I think this isn't well-applicable to machine learning because if there are more data sources to be considered (like other cars' behaviour in addition to just c

I think this isn't well-applicable to machine learning because if there are more data sources to be considered (like other cars' behaviour in addition to just camera images), they'll be always considered anyway. For humans, it requires considerable additional effort to do so, but for machines it's fairly cheap, so why not do it anyway? The uncertainity part should ideally, in my understanding, be represented by confidences returned by neural networks. They don't claim "there's a crossroad ahead, and not a tollgate" but rather "85% match for crossroad, 30% match for tollgate, [...]". If those results are not distinct enough, the surrounding application should probably go into a more cautious mode and slow the car down to begin with. I suppose that's what such systems do, but maybe someone with more field knowledge can confirm/negate that.