I don't know how the system is currently implemented by automatic traffic signs recognition is already possible, see for example this video: https://www.youtube.com/watch?v=hU7yHQkg-7U
I think that is another argument against Google's approach. If one can reliably detect traffic signs, why build that database? For traffic signs, one could argue that it helps increase recall; if the car knows the locations where traffic signs were seen earlier, it can decrease detection threshholds for those locations. However, the car still would have to be able to reliably detect new signs (say for a temporary detour) on first sight.
Things are way worse for all kinds of changes to roads. Even if the first car correctly classifies that white spot on the road as a lost paper that it can drive over, what good does that do the next car? The wind may have blown it away or to a different location and into a different shape.
To me, Google's approach seems an attempt to build a model of what the entire world looked like a short while ago, while cars only need a rough model of what it looks like now.
Scaling Google's approach to millions of cameras in million of cars may improve the model and decrease its latency and might make the latency low enough, but I don't see why it would be the best approach.
Things are way worse for all kinds of changes to roads. Even if the first car correctly classifies that white spot on the road as a lost paper that it can drive over, what good does that do the next car? The wind may have blown it away or to a different location and into a different shape.
To me, Google's approach seems an attempt to build a model of what the entire world looked like a short while ago, while cars only need a rough model of what it looks like now.
Scaling Google's approach to millions of cameras in million of cars may improve the model and decrease its latency and might make the latency low enough, but I don't see why it would be the best approach.