I haven't seen reports that any self-driving cars learn to drive better in real time. Instead the data is collected and used to improve the software. There might be frequent software releases (perhaps even every day) but this isn't quite the same thing.
An offline learning process seems considerably more predictable. The release engineers can test each release to see how it would behave under various extreme conditions.
It's not clear that there's a compelling reason to switch to true online learning. Self-driving cars will improve over time, but not to the point where new training data gathered at 9am should make a significant difference to how the car drives at 6pm. Probably this year's model will drive better than last year's, but the changes are likely to be gradual.
As you say, there are diminishing returns to gathering more data, which suggests that in many domains, updating the software in real time won't be a competitive advantage. Unless it's a system that's reacting to breaking news, the delta in training data gathered in the last 24 hours is unlikely to make a huge difference.