Public perception doesn't matter that much because the customers are car makers. What matters is their technology, which seems to be state-of-the-art and certainly better than what Tesla has.
Is Google ahead for a final product? Quite possibly, but they don't have a product and Tesla doesn't look far behind sortware wise, though it is hard to tell.
Krafcik also mentions they are making the lions share of their progress now using their simulation engine, where they can model every conceivable variation of any bizarre/difficult encounter they have on real roads. In sim they accumulated a billion miles in 2016.
This never made sense to me. You certainly need enough data, but how you interpret and process that data is far more important.
Think of it this way: if Tesla wants to test a particular algorithm for a particular driving situation, they can "play it back" over an enormous amount of real-world situations. They will have tons more potential edge cases with which they can validate their algorithms.
Big data is no where near as much a competitive advantage as it was three years ago. It seems not everyone outside the field has noticed that though.
Image classification seems like it would be very different, most importantly that 99.9% "correct" would be a great achievement, but for self-driving cars a .1% failure rate would be completely unacceptable.
Please oh wise ones how do we simulate nlp data, numeric data, finance data, biological data and anything else machine learning is used for.
Oh you are able to classify dogs and cats in images after a 2 hour youtube. How nice.
Renesd is correct that "big data" is overblown. There are diminishing marginal returns - you need orders of magnitude more data for the same incremental gain (and this blows up well beyond however millions of cars Tesla can hope to run).
You're correct that data augmentation is only a marginal technique to squeeze out more performance, and not generally possible in many domains.
Sure, they can push a beta algorithm to cars and record high-level decision making between human & algo, verifying it's not totally out of whack. But that's hardly something that is going as training data into the models.
Waymo's autonomous platform is a frankenstein of various machine learning techniques, and much of it isn't glamorous, it's less contigent on big breakthroughs than it is on elbow grease. Google demoed as proof-of concept full autonomy in 2012, and much of what they've been doing in the 4 years between then and now is the tedious job of addressing and validating their system across the full spectrum of edge cases that must be dealt with if they ever hope to foist their safety critical software upon the public.
It's not clear to me that Tesla's current development paradigm will ever be sufficient to completely take the human out of the loop. Tesla's approach is incremental, and I suspect they'll have to make some big changes if they wish to fully close the gap. Waymo has kept their eye on the prize from day 1.
Big public opinion perspective here too.
Other companies seem to be taking a generalized AI approach. Their cars work everywhere, but how well?
It'll be exciting to see which approach works best.
Having high res maps certainly helps, and if you have that data, then why not use it?
It is no different than what people do. If you drive the same route all the time, you have some expectation of what lies ahead.
You are wrong[1]. Streetview vans have been outfitted with LIDAR for years and generate 3D models as they drive. Just because you do not see the point-cloud data in your browser doesn't mean Google doesn't have it.
I'm sure someone built a hack that let you explore that directly.
http://callumprentice.github.io/apps/street_cloud/index.html
https://flowingdata.com/2014/03/26/reconstructing-google-str...
This has to be really old information, though. They have that video of the car detecting school bus stop signs and a police officer directing traffic. A stop light is child's play after that.
If Google has an approach that works for them and it depends on the maps - that's fine. I'm just pointing out that they (probably) made a decision a long time ago that they are going to use those maps to further their self-driving technology. It's a design choice, with pros and cons, like any other decision.
A big advantage I see in Tesla's strategy is that all of their cars are now shipping with full self-driving hardware. Even if that hardware isn't actually used to control the car, Tesla will have an order of magnitude more real-world data than anyone else.
[1] https://www.theguardian.com/technology/2016/jul/01/tesla-dri...
I'll bet the number of people that buy a individual Intel CPU are a fraction of a percentage of the general population. So why do I see Intel ads on television?
To answer your Intel/TV ad question more directly: because there are a lot of people on Intel's payroll whose salary directly depend on them placing ads on TV.
I do not demand you see my perspective, not even sure that's how I see it. But really, how do we know that Intel's branding campaign was valuable to them? It seems obvious, but if it's super obvious, it should be easy to explain.
It's very much a binary situation like Siri. Either the technology works and is 100% effective under all conditions or it doesn't. If it doesn't then people aren't going to look at it as a key purchasing differentiator because they won't really rely on it. Because of this I think we have at least a decade if not more before it matters who is winning the race or not.
And I have blankets that I sleep great under in winter and summer and others that make me wake up in a puddle of sweat. And some I take camping and others that are very comfortable but very delicate.
So no I wouldn't say that anything I own works 100% of the time. Some work more than others, and good stuff generally works in several situations (or not, when it's optimized for one use case) - but overall, if your threshold for success is 'pretty well', then any self driving tech that isn't worse than humans would be good enough.