The fleet of Tesla cars on road is an advantage that Uber and Lyft have over Google. They can deploy cars with a lot of sensors on the road AND make money off of it for the most part!
If data is the differentiating factor in this game, Google has less of it! Which is interesting position for Google to be at!
> If data is the differentiating factor in this game, Google has less of it!
If you're talking about Uber and Left, I think Google currently has them beat in terms of data on a global scale, given how long they've been collecting data for Maps. You're talking about a future scenario where Uber and Lyft have rolled out significant numbers of sensor laden cars, but right now they don't have that - and who's to say Google won't have another approach by then.
Google's map data is in no way comparable to the data collected by Tesla's fleet (expert trained by vehicle owners), which by the way is gathering a million miles of experience every 10 hours.
True, but Google's Street View cars have 360 degree cameras and LIDAR systems on them and I assume the data is saved at full fidelity and sent back to Google.
Teslas definitely have many more miles on them, but I don't think the cars are sending back every single frame captured by its cameras back to Tesla HQ.
Privacy issues aside, the data volume would be far to high for the cars mobile connection to transfer. But if they only send whenever the actual and the expectation differs, e.g. the cars GPS track deviates locally from the map data, or whenever the driver disagrees with the autopilot, they can get a lot of information.
With this new usage of the radar, they seem to create "radar maps" of all radar echoes from bridges and traffic signs. Using those maps they should be able to detect true obstacles with a high enough confidence to enable automatic braking/evasion. The problem so far with automatic braking by radar is not so much with detecting possible obstacles but with false positives. A car must not randomly brake when there is no obstacle.
I think both sides have their pros and cons. Google has high resolution data but it's outdated where as teslas data is lower resolution but always being updated. Tesla has the edge here since they will know about road construction and such well before Google does.
Im sure this will flip one day when if Google increases their map fleet
Which is a bit circular - lot of things are easy if you can detect "interesting", but like with pornography, this is one of the "I'll know it when I see it" AI-hard, human-easy tasks.
...but Google's data collection specifically covers whole areas. Tesla might be gathering millions of miles, but they could all be the same miles on the same road, like someone's daily commute.
Most maps products get traffic data from the same external vendors (like TomTom). I think they get it from dedicated sensors like counting cars on street cameras.
Obviously you see Waze info on Waze itself but it has to work without any other users nearby. (Btw I don't think Waze and Google really share all their data, Waze doesn't even have lane routing.)
And how many rounds of additional fund raising is required for Lyft or Uber to buy enough sensors to retrofit 1% of their vehicles? What happens if drivers decide to sell those sensors?
I think that the loss of the population of Uber and Lyft drivers would be a severe problem, assuming that Uber and Lyft drivers form a core advocacy group.
"Developers" ( in the sense of that Ballmer clip ) and sysadmins created a critical mass that carried Microsoft forward.
They have big fleets? Where? For the most part the uber cars are not owned by Uber and certainly not equipped with any sensors. I know they have some tech under development but that's not the same as a massive fleet on the road.
If data is the differentiating factor in this game, Google has less of it! Which is interesting position for Google to be at!