Being prepared as a second mover once a trailblazer demonstrates a good path can be good business. It even has some advantages over trying to lead and possibly wasting lots of R&D money on dead ends.
1. Driving automation is orders of magnitude more expensive
2. Improperly-tested vehicles are much more likely to have fatal consequences, meaning a 90% perfect smartphone is OK, but 90% perfect driving software would kill people
3. It's much, much easier to clone an iPhone than Waymo hardware + software
Plus the amount of patents involved in this mean even if you're burning money down a dead end you might stumble upon an insight / patent that would help regardless of the approach taken. Often time with engineering a good design comes out of a known shortcoming of some other subsystem.
Only if you pretend it didn't take decades of gradual improvement to make the iPhone possible, and ignore the epic manufacturing cost of the components. Basically only if you ignore everything that has / does go into smartphones. It took hundreds of billions of dollars of investment into the underlying iPhone components and their near ancestors, to make the iPhone possible.
What's the total cost of the specific driving automation segment so far? A fraction of all the money plowed just into semiconductor fabrication development and construction over the last ~25 years so we could have smartphones at all. Then move on to the investment into storage, software, glass, communication chips, security, glass, etc.
It'd be equivalent to pretending that we're going to leap from Tesla's hands-free to full automation without anything inbetween. The iPhone didn't materialize out of nowhere.
There are billions invested into electric cars, too, but every large company has access to that technology (either with acquisition or licensing).
To understand what the difficulty is, it's important to consider that the size of the sensor input is very large. Don't think of it like twenty range finders around the car, rather a 360 degree medium resolution color + depth image (about 0.5 million data points coming at 30 fps).
It's difficult because you will never encounter the same set of sensor inputs twice, so you can't treat it like a search space problem. Once you've accepted that, you're in AI/ML territory where you might try to reason about what the closest set of known sensor inputs and action would be (classical AI, expert system), but that is impractically difficult with as 0.5 million dimensional search space, or train an ML model to 'reason' about the sensor space to make a decision about the appropriate action.
Approaches using a small number of sensors can do automatic breaking and smarter cruise control, but haven't been seen to be successful about navigating and making strategic decisions. The current belief is that more can be done by using denser sensors and more data and seems to be the case. There are people working on reducing the sensor density requirement, but the main focus right now is building a successful and safe self driving car, regardless of sensor and compute costs.
It's probably a very valid tradeoff.
In the future we'll likely have super-human spatial and temporal resolution, right now more improvements have been gained from highest possible spatial resolution with minimal plausible temporal resolution.
I hope there is a better, more technical explanation that ML researchers are using, because as someone who is somewhat of an expert on human vision and building products around it, this foundation is godawful if it is to be taken at face value. Which again, I am sure this is a simplification. Or at least, that's what I am telling myself.
File the promises and the problems under fiction because it appears to be more important to keep the world order, its financial system and these ridiculous media darling fluff piece corporations alive while they bleed money.
And no, im not closed to the idea of successful work being done on automated driving but 30 fps, WTF? too much going on in the larger context of the world, this shit isn't happening in 2020 or 2024 or whatever else many might say.
Because of this I'm leaning towards thinking waymo isn't trying to mimic actual human input.
https://www.theatlantic.com/technology/archive/2017/08/insid...
The problem with Uber is that they already operate on a trust deficit. They've had a long history of ignoring local rules and had a string of high-profile exits - including the CEOs - after workplace harassment issues.
That's not exactly a company I want to trust with my life.
Waymo, otoh, is operated by a company known for its "braininess". Self-driving is a computationally challenging problem. I trust Google to solve it much more than Uber.
There's also the fact that Waymo doesn't have to be a profit center for Google at all. They can afford to incur losses for years until they get the technology perfect.
For Uber, self-driving cars HAVE to be a profit center. If the company's existence depends on it, can I trust them to not cut corners?
Car manufacturers have decades of experience in shifting danger from inside to outside. Passengers are not the ones we need to worry about.
Passengers are not the ones we need to worry about.
Driving is very dangerous, and most drivers don't even carry enough insurance to pay for a couple months time off if they hit me. This, essentially, make my injuries an externality that isn't priced into the cost of transportation.
For that matter, if I'm injured in an accident and it's my fault, I don't have a lot of coverage for myself; I mean, through work, I have a long-term disability package, but that's still not great compared do what I'd make were I not disabled, and disability, as I understand it, is a huge pain in the ass when it comes to you being, you know, 'partially disabled' - still able to work, but not able to make nearly what you made before.
Carrying adequate insurance insures that the total cost of the injuries caused is rolled into the operating cost of the vehicle, and helps to price it rationally.
The first iPhone was revolutionary and ahead of its time, but copying a user experience, a grid of square icons, fancy animations and double touch is a lot easier than copying Waymo. You can't just copy Waymo by looking at it like you could by looking at an iPhone. Waymo's code is all in a black box of compiled code and how it works really is anyone's guess besides neural network and all the captors and millions of hours of simulations and drives etc.
The point is that this data would have been collected by the first-movers the entire time they are on the road. Such data would not be made available to newcomers.
If a new self driving car company would join the scene, they would need to gather all this data again themselves, somehow. This is the "uncopyable advantage" mentioned by OP.
Other aspects of a self-driving car business are more easily observed, including the significant regulatory and public perception risks. Here's one scenario: Waymo creates a technically excellent self-driving car, but it kills a handful of people in particularly gruesome way that causes the public to lose trust in a fully autonomous vehicle. As a result, "partially autonomous" vehicles are perceived as "just as good", and a company that has a massive ride-hailing business through which to monetize the technology has an advantage over another that has only a theoretical technology advantage.
Where would you put the odds of a regulatory or PR catastrophe causing an existential threat to Waymo?
And Comma.ai is cameras only, so it’s not fair to say all the others believe LIDAR is necessary.
Also, just because someone has LIDAR on their test rig, that doesn’t mean they think it’s necessary for production vehicles. They could just be using it for validation. They also might start off using it, and wean themselves off as they progress through development.
Waymo I believe had explicitly said they think LIDAR is needed but I haven’t heard anyone else say that explicitly.
The last major Tesla crash in California was a clear situation where LIDAR would've prevented a crash and yet the Tesla team continues to insist that cameras and radar (sonar really doesn't help much...) are all that is needed.
You just described Palm, Blackberry, and Windows Mobile...all of which Apple was copying. The iPhone was evolutionary, but not revolutionary by any means.
Back on topic: It's more about letting the first mover spend the money on figuring out the business plan. The second mover can then jump in with a more efficient business model based on the lessons learned from the first mover.
The biggest hurdles are at the start, but any company that can demo an autonomous vehicle that can, on average, drive a few miles in urban traffic without getting hung up is off and running. After those initial big hurdles, it's small hurdles stretching off further than the eye can see.
Uber had never really learned the first big hurdles. They scaled too big, too fast, and the whole operation was a clusterfuck. The rumour is that Uber was having trouble getting simulation working for them. So early on with Uber, driving around Philly, they were disengaging every block or two, and 2 years later they weren't doing much better.
In the wake of their accident they've had some time to reevaluate everything, and we'll see if they've actually managed to sort out their problems.
Waymo leads right now with 7 million miles driven.
If you wanted to drive that many miles in the next year, how much would it cost?
I estimate it would cost maybe $60M or so:
-$30M on cars (assuming a fleet of 200 cars driving 100 miles a day, costing $150K each)
-$5M on safety drivers (assuming $20/hr, 30 mph)
-$1M on fuel (assuming 30 mpg, $4/gal)
-$5M on insurance (no idea)
-$5M for a garage
-$5M for a couple dozen techs/engineers working in the garage
-$10M overhead, supplies, other costs?
And this total of ~$60M is with a bunch of upfront fixed costs (mostly cars, but also the garage). Year two the cost would drop in half to ~$30M.
By my math, if you had a working system and were bottlenecked only by data, you could catch up to Waymo's 7M miles for just $60M. Maybe my estimates are way off and actually it's $100M. Or even $200M. That's expensive, but I'm not so sure it's a moat when we're talking about companies with 10s of billions of cash on hand chasing a market that could eventually be a trillion dollars.
At this stage, I think if you have a working system, then by my math the cost per training mile is well under $10 per mile.
I wouldn't call that a moat, but maybe you do.
You did miss in your cost estimate scaling the compute costs of ML training to ingest that data. Also test courses and simulation to amplify and elaborate on tricky cases found in the data. Adding those things in adds 10's of millions to the estimate but doesn't change the fundamental analysis.
I think the real moat is in fleet networked data. If you have a lot of cars on the road and they are sharing info about strange situations to expect, it could be a big advantage for how "smart" the driving seems. I am thinking things like broken stoplights or badly placed traffic cones. A networked solution can alert other cars of the puzzling condition and the interpretation. Then later cars passing that location can proceed more confidently than if each vehicle has to work out an interpretation itself.
Just like how we see all newer cars coming out with the same types of tech e.g. lane change alerting or backup camera monitoring or Android auto/Apple carplay etc, as standard features? Or is automation tech more of a first mover monopoly in and of itself since it's so unique and patentable?
A lot of the current driving assist features are made by the same companies (e.g. mobileye, bosch) and then installed/licensed to car manufacturers. The same thing could happen with self-driving depending on who develops it first.
I think in general, it's easy to overestimate the long-term value of a first-mover advantage.
Sure, anyone can build a search engine. But as billions of dollars in spending has shown, no one can create one better than Google