You might be forgetting about Google Maps vans.
Which is to say, if Google wants self-driving Google Maps vans, then collecting data using Google Maps vans makes sense. But if they want general-purpose self-driving cars, then collecting data using Google Maps vans will only give them a very narrow set of data.
Definitely two data sets that should be put together.
I'm assuming that once a Google Maps van has covered an area, it intentionally avoids that area until a significant time later.
In the OP where I mention outfitting an example 2019 car, that car might be a current-model-year car with roughly similar vehicle dynamics to the proposed 2019 model. The only thing that they are paying particular attention to is the specific sensors in use and their placement on the vehicle. This apparently is critical to development of the driving model, the camera on the test vehicle must be in the precise position and direction and must react in the same way as the production model or the data is bordering on useless.
Now, with a full 3D pointcloud and enough sensor data you might be able to translate one recording into a lower-resolution resampling to model the production version, but I've not heard of anyone doing that. Test data is collected on the production sensor suite, no changes allowed.