Inertial navigation systems [0] use integration to calculation position, so errors add up quickly. The first in car navigation system [1] used inertial navigation. I think it used places where it knew the vehicle would (should?) stop as waypoints where the velocity could be set to zero. For example, if we think we're near a stop sign, and our calculated velocity is zero-ish, make it zero.
It's pretty amazing how accurate smartphones can be when you're looking at your position while moving in an area that doesn't have GPS, probably using similar techniques. The mobile device knows (is guessing) you're in a vehicle (using measured speed, Bluetooth connection to a vehicle, the fact that you're on a road, etc.) and can snap your location to a road on a map, as well as project your position.
It makes sense that inertial error could be similar for similar movement patterns, say driving over expansion joints in a concrete road. A ML model seems like a good way to try to compensate for that.
The reason I wonder if Apple is doing this already is they have all the pieces...they even know which vehicle I'm in, because I pair with the Bluetooth system in both vehicles. So they could actually build a model per vehicle, which I imagine would be more accurate.
They do have a patent pending for applying machine learning to the location domain, looks like this one is for GNSS though: https://patents.google.com/patent/US20200049837A1/en?oq=US20...
[0] - https://en.wikipedia.org/wiki/Inertial_navigation_system