> I realise it's not apples to apples and that Google's cars may be more autonomous for now.

I think that is an understatement. This video shows why staying in your lane on a highway (what Tesla autopilot does) and dealing with city streets are two just completely different things: https://youtu.be/tiwVMrTLUWg?t=8m49s

I think Tesla has a ton of catching up to do to compete with where Google is right now.

Tesla's auto pilot isn't at all comparable to what Google are doing. It essentially the same as the adaptive cruse control (cruse control that adjusts to traffic in front) and lane assist (auto stearing to stay in lane) you can get from most other car manufactures as an option.

That's not to say that tesla isn't developing full automation it's just that what they sell now is just clever branding on something you can get elsewhere.

I believe that at some point in the future they will launch a joint-venture for a fully automated Uber competitor with Tesla handling designing and building the vehicles and Google providing cash, self-driving software and building the actual app.

Musk is literally too good friends with Sergey Brin and Larry Page for this not too happen.

http://motherboard.vice.com/read/elon-musk-and-larry-page-ha...

http://www.businessinsider.com/googles-secret-apartment-elon...

Data is the ammo that will win the war of self-driving car companies.

Tesla, with a large, update-able fleet in the field, has a much larger data-collection platform than Google. Even if they're not using it for full automation now, the collection itself is very valuable.

This is almost certainly why Uber is putting self-driving cars into the field now as well, even though they are clearly not ready for prime-time. They need those sensor packages to be driving around on real roads and in real traffic.

But in terms of collecting training data to develop the algos that will eventually be fully autonomous, Tesla is way out in front. That's what the OP meant: every Tesla is collecting information that gets sent back to HQ to train the neural nets (or whatever) that will eventually drive the cars themselves.

200M km of adaptive cruise control & lane assist on a highway is very different from 2.7M km of fully autonomous driving on city streets & highway.

Think of the data collection aspect of it, though. Tesla's driving AI will know what happens in the real world better than Google's driving AI does.

I think the general case—navigating unusual terrain, intersections, traffic patterns, changes in aggressiveness tolerance, construction, adapting to "normal" problems like needing to turn left illegally at a stoplight when the oncoming lane is continual—is almost entirely unrelated to where Tesla has an advantage. This entirely autonomous general case seems to be where Google is attempting to dominate.

How much data are each of Tesla's cars really collecting vs each of Google's cars? If the data is not useful, it won't matter how many miles they've driven. Do they have the same sensor capability and collection?

Imagine the difference between, say, trying to determine a user's video preferences based upon their hit statistics on IMDB vs direct viewing data from Netflix that includes everything they've watched, how long (1 minute vs the entire show), when, the order they've watched (eg, mood predictors), frequency, etc.

I'm not saying that this is the difference between Tesla and Google, just that quantity and quality don't necessarily equate.

Not really, because by comparison Tesla cars are relatively blind. Google's cars are loaded up with all kinds of sensors collecting as much data as they can. And they have the advantage of driving back to the plant to hoover off the data with as fat a pipe as they want.

Tesla is far more limited in both what it's collecting and how it's collecting it as it needs to go over a cell signal. For example they are using a camera for a hefty part of the autopilot system. They obviously can't stream every frame of that camera up to their servers for deeper analysis. Besides being infeasible bandwidth requirements it would be an insane privacy violation of the owner.

Does Tesla collect data when Autopilot is turned off? Otherwise I would be very curious to know how many miles of non-highway driving it has logged.

I don't know, but they do have a history of logging a lot of data. They've used some of the logged data to rebut high-profile critics in blog posts in the past, in cases of accidents or low-mileage claims.

Data is a commodity.

Tesla may have an order of magnitude more kilometers, but the quality of data that Google is collecting is (IMO) more than an order of magnitude superior per kilometer.

Tesla (currently) has one radar, one camera and 4 short distance ultrasonic sensors. Google has LIDAR plus a lot more.

Tesla's suite may or may not be sufficient for operation. But for training, good data is critical.

The requirement of what you need to compare with that data though, is important. Tesla's cars are training off less detailed data, yes. But that also means they're designed to work with less data available. Google's cars require $150,000 worth of sensor hardware. Will that price go down, sure? But Tesla's will be even cheaper.

Google could easily build their software model so that it only uses a subset of the available sensors. They can then use the full set of sensors to judge the effectiveness of that model.

Tesla can't do the inverse.

I think that in 10 years time, time of flight lidars will become ubiquitous, just as common as digital cameras are now. And this will usher a new revolution of interaction between reality and the digital world.

Teleportation, obviously.

.

.

.

Oh how sweet it would be

for it to be me

that's the one to reveal

in days before

the IPO of many of these

(and the main other 3)

that what the world

has been waiting for

is sitting with me--

with open arms--

desperately ready to be--

with you--

always.

.

.

.

Some say with wonder

other with haste

all that's to be said

but even in this case

where is it again

what makes it all

seem sudden

a change

leaving the world a part

tense at the seams

knowing even then

aside from the waves

crashing toward the bitter end

it was dealt

squarely

only pausing to be

as there it is again.

It's not just going to be about straight miles driven, it's about the technology being focused on and what's possible with it. Both Tesla and comma.ai are focusing primarily on what the car can see and do with it's own sensors... Google's cars are only functional on roads excessively mapped far above the normal Google Maps level. Google's existing strategy for self-driving cars isn't practical at a national level because of that extensive mapping requirement, and it possibly never will be. (Google's cars may have driven x number of million miles, but it's all the same very small number of roads.) And while prices drop as technology develops, bear in mind that in addition to all of that, Google's sensor platform is the most expensive out there.

Additionally, I've read some really interesting articles about research other car manufacturers have done. For instance, Google has never tested in bad weather, but Ford has been working on self-driving cars that work in snow. And while Google just assumes the humans are meant to be 'along for the ride', Volkswagon did some really good UI work, in terms of figuring out how to make the car's actions predictable, and hence, less scary. (Essentially, the car indicated to the driver what it was about to do before it executed a maneuver.)

Google is really good at capitalizing on their self-driving car project for marketing purposes, but it's extremely unlikely it'll ever be a market leader.

>> Google's existing strategy for self-driving cars isn't practical at a national level because of that extensive mapping requirement, and it possibly never will be.

>> Google's sensor platform is the most expensive out there.

>> Google has never tested in bad weather

What about a self-driving cars as a service ? they can be the first to start a very profitable service that is limited in area and in weather even thought the sensors are more expensive(and they can claim "we aren't cutting corners like everybody else!")

And that could be a great place to be in, strategically.

A few years ago I was 100% gung-ho about Robotaxis, and I went through a mild depression last winter when I gave way to mounting evidence that it's a really hard problem and the 'it's 30 years out' naysayers are probably correct. It just sucks being wrong.

Google's Koala cars are functional under only the most idyllic, constrained and carefully monitored conditions. There's a huge laundry list of unsolved, and unknown problems between what Google has demonstrated so far and where they need to be technology-wise to run a robust, reliable, profit generating service at the scale needed to cover their R&D.

The casual thought experimenter generally fails to recognize the frequency with which they utilize higher level reasoning when driving that's well beyond the limits of the current state of the art in AI. Nobody has the slightest idea of how to solve this, let alone dig into all the as-of-yet not understood logistical problems inherent in commercializing the technology, an unexplored realm rife with any number of unknown unknowns.

The real world is a very messy place. Unlike Google, Uber is eyeballs deep in the messiness of the real world, so they're probably better poised, though a lot can change in 5 or 10 years. The competitive playing field has been so dramatically altered in the past 2 or 3 years that the days when Google was the only company anyone took seriously feels like ancient history.

With regards to the sensors, my bet is that by the time AI's capacity to reason is where it needs to be, the sensors and software needed to see and interpret the dynamic driving environment will be dirt cheap. Probably all you'll need is cameras, their cost keeps going down and the state of the art in image processing is progressing and will continue to progress.

Sure, they could definitely do similarly to what Uber just announced, in a small scope area like the Bay Area suburbs. But that's not likely to be a high margin product, certainly not something Google would want to hang onto long term. It's far smarter to be selling software licenses or cloud access to millions of units built by other car manufacturers and operated by individuals or other companies at scale, that's where the money is.

But you also have to realize that you'll also highlight the weaknesses of the technology. People may not be able to easily specify to the car where they'd like to disembark. What if people want to be taken just outside the service area? Uber is including a human with their self-driving project for now, which doesn't save them (or you) any money.

Google learned from Glass that a small number of users and a lot of public attention and hype about a product can quickly eviscerate it. The technology was good, but people without hands-on experience misunderstood it, and a couple small incidents became national news. A small rollout can just as easily kill your project as kick it off.

Even if Google decides to sell access to other companies, at scale, they can still say: "our service only works in summer , in that list of expanding areas". And of course if they sell access on a per-trip-basis, it's almost as if they own the service themselves.

>> What if people want to be taken just outside the service area?

Google is currently trying to be the comparison search engine for people who want to order rides - via their Google Maps. If they sucsseed, they'll just fit you with the right service according the limitations of the self-driving car ,etc.

And regarding Google Glass - IDK. Even Uber is marketed on a city-by-city basis,

Tesla is either already mapping roads through the sensors on their cars, or is only one software update away from doing so. Their mapping data will be a lot fresher than Google's.

This is true but mostly irrelevant, because mapping data is the least valuable self-driving data. Google focuses on it because Google has it already. Google Maps is one of the things Google has that few else have, so it's obvious for them to build their technology on it... nobody else can copy them.

But if you have the most cars collecting the freshest data, you still have to bear in mind, the car collecting that data, is currently relying on the old data. Which means your cars can't trust the map data. And the reality is, with how much things in the real world change, your map data will never be trustworthy. You can have it, but you can't rely on it.

Which is a better self-driving car? The car that can look at a cloud-based 3D map of every object in the entire town, assuming it's current, and decide on a route? Or the car that can ping Google Maps, get told "turn left on Main, right on Washington" then entirely from it's live surroundings, decide how to drive?

Tesla cars don't have enough sensors to capture great data. It will require a lot more than a software update for then to record data on par with googles quality

They claim autopilot uses "cameras, radar, ultrasonic sensors and data."

I think even just the cameras alone would be valuable. It seems a Tesla has a better idea of what's going on around it than a human would, so these sensors should be at least close to sufficient for training an autonomous driving agent.

> Google's cars are only functional on roads excessively mapped far above the normal Google Maps level.

Since this is a core part of your argument, I'm gonna go ahead and [citation needed]

I've not heard anything of the sort and I've seen Google's cars on all sorts of roads. Plenty of their talks have been about the cars detecting anomalous situations and reacting appropriately, as well.

There's no reason to believe they've exceeded this limitation: http://www.slate.com/articles/technology/technology/2014/10/...