A small note, but GPS is only well-approximated by a circular uncertainty in specific conditions, usually open sky and long-time fixes. The full uncertainty model is much more complicated, hence the profusion of ways to measure error. This becomes important in many of the same situations that would lead you to stop treating the fix as a point location in the first place. To give a concrete example, autonomous vehicles will encounter situations where localization uncertainty is dominated by non-circular multipath effects.
If you go down this road far enough you eventually end up reinventing particle filters and similar.
Vehicle GPS is usually augmented by a lot of additional sensors and assumptions, notably the speedometer, compass, and knowledge the you'll be on one of the roads marked on its map. Not to mention a fast fix because you can assume you haven't changed position since you last powered on.
None of the inputs you mention work against multipath effects in cities, which means car GPS won't know which lane you're in and in a grid system may think you're on the next street over.
If you have an HD map you can solve for it using building shapes or by looking at the street with cameras. WiFi seems like it would help, but the locations of the WiFi terminals are themselves based on crowdsourced GPS.
If you go down this road far enough you eventually end up reinventing particle filters and similar.