Right.
Same observation from far upthread.
Maybe you pay for it one way or the other?
High res vision data (space and time) and crazy detailed and advanced feature and AI modeling.
or
Multi-sensor data that must be synchronized together and then modeled together in AI.
No free lunch?
If I had to bet today, I would go with the multi sensor approach with the reasoning that it brings more different kinds of data together (expands feature dimensions in informative ways that is more difficult or not possible with a single sensor type). And hence has more discriminatory information than single sensor.
And, after you have arrived at some solution to the AI driving problem, you can then optimize by trying to simplify the data and model by removing data that are less/not important and focusing in on the aspects that are more important. This could result in simplifying data processing and/or removing sensors.
Tesla is still playing with Lidar, so they haven’t removed it yet from their AI modeling, as best i can tell. They are correlating it to their camera data. But maybe they have advanced in this optimizing step.
As my comments here and upthread show, there are arguments both ways.
Again, do you have something specific to say here about the relative analytic complexity of the different players?
If not, I don’t think we know which systems have more/less analytic complexity/sophistication or however you might define it.