Lyft speeds ahead with its autonomous initiatives | Hacker News Reader

Lyft speeds ahead with its autonomous initiatives

techcrunch.com

99 pointsapancik90 comments

> The first car from Lyft’s Level 5 self-driving initiative will be the Ford Fusion Hybrid. Lyft’s use of a Ford Fusion apparently isn’t associated with the partnership the two announced last year. Other AV companies have used the Ford Fusion as a platform for integrating self-driving technologies

Why are we still talking about Level 5 autonomous driving when we can't even get Level 4 working properly? I believe this is sending the wrong message.

On the topic of the acquisition, it seems like a good strategic buy for Lyft. I am still not sure whether they have the capital nor the talent pool to develop a strong autonomous vehicle product. It also seems like they are a bit late to the party, as there are more and more doubts on the reality of self-driving in the next few years.

I may sound sceptical, but I am just cautious when it comes to news on self-driving cars. However I am genuinely excited at this development and look forward to hearing more about how Lyft is progressing on this quest.

"Level 5" is the name they've given that division - as much a goal as anything. I doubt they'll launch at anything near true level 5 capability

>Why are we still talking about Level 5 autonomous driving when we can't even get Level 4 working properly? I believe this is sending the wrong message.

i think it is capability gap between tech companies and car companies. Car companies can't even get cameras around all the body to avoid accidental scratches during parking. Where is tech companies, while could easily do a lot of car tech, have no business case doing anything less than Level 5 - the Level 5 is a tech platform where is anything less is an advanced car, and the tech companies are in the platform business, not car business.

Traditional car companies are also working on autonomous vehicles.

There is a lot of business to be had with Level 4. While the car may not be able to go everywhere, in restricted areas a robot-taxi/shuttle service would be a great advancement. We would all love to be at Level 5 but I would take a solid Level 4 in a few years instead of waiting much much longer for a viable Level 5 solution.

> Car companies can't even get cameras around all the body to avoid accidental scratches during parking.

This has been a feature introduced by most car companies in the last ten or so years.

By the way, I don't think any Tesla car has this.

Uber and Tesla are dead last in self-driving tech, beaten by everyone from Hyundai to Baidu-BAIC. I don't know where you get the idea that "car companies can't even get cameras all around the body to avoid accidental scratches." The leaders of the industry are Daimler-Bosch, GM, Waymo, Volkswagen, Ford, Aptiv, and BMW-Intel-FCA. Mostly car companies.

> In effect, every Lyft vehicle in operation today, with a smartphone on the dashboard, could be commandeered to become a “camera” watching, surveying and mapping the roads that those cars drive on, and how humans behave on them, using that to help Lyft’s autonomous vehicle (AV) platform learn more about driving overall.

Another instance of the "more data is better" fallacy. Humans can drive cars safely after only fifteen years of intermittent sensory input. Lyft could collect that "data" within just one year employing ten collectors. That still doesn't give you brains.

Is Techcrunch an outlet for PR pieces? I'm asking because that article reads like one of those.

Yes

How is having recorded video of actual roads not valuable data? You're assuming the only option is training self driving cars on it. They might be training something totally separate to recognize signs, or see damaged roads, how quickly pedestrians react at different times of day, etc.

Very narrow view point for you to take.

I agree that having detailed maps is an advantage. But they only make you a better driver in the places where they are correct. As such you can't rely on them to learn to drive, because you must be able to adapt to changes. Driving is not about the best case, but about the worst case.

Using uncalibrated and random positioned phone cameras to learn about driving a car that has better sensory equipment seems backwards to me. But point taken, the article says "learn more about driving overall." So that could be anything.

To reiterate, you cannot, ever drive based on any map, regardless of how many smartphones collected that map's data. You might use a map for navigation, but even then you'll have to deal with closed roads or changed traffic flow that isn't yet on the map. You cannot use maps for driving.

Well that's my sentiment too. But the article is wishy washy on the use of the collected data. Having local knowledge can make you a better driver after all.

> Another instance of the "more data is better" fallacy.

More data is better. The fact that humans have better algorithms that need less data does absolutely nothing to negate this.

Depends on whether it’s the right data, or meaningful data. The idea that Lyft needs this acquisition in order to implement video capture from its drivers’ cell phones is laughable so something else is going on with this acquisition. But to your point, the assumption that this is even a problem of “not enough data” is questionable at this point. How to turn that data into results is something no one has come close to figuring out yet.

> Depends on whether it’s the right data, or meaningful data.

Street level mapping data isn't relevant or meaningful? Basically every company working on this problem seems to pretty strongly disagree with you.

> But to your point, the assumption that this is even a problem of “not enough data” is questionable at this point. How to turn that data into results is something no one has come close to figuring out yet.

This is trivially false. Given infinite data, all possible situations would be represented in the data, and the solutions applied in those situations could be copied exactly, something that existing algorithms are completely capable of doing.

Given infinote fata, infine storage and infinite computing you would be right. In practice it means you are wrong. Feeding more data does not necessarily help given a finite amount of computing power.

>> This is trivially false. Given infinite data, all possible situations would be represented in the data, and the solutions applied in those situations could be copied exactly, something that existing algorithms are completely capable of doing.

In principle. In practice, you'd need infinite time and infinite storage.

Btw, do you have to add stuff like "This is trivially false" to your comments? It doesn't make your comments sound more right, only less well considered.

> In principle. In practice, you'd need infinite time and infinite storage.

That is irrelevant.

> Btw, do you have to add stuff like "This is trivially false" to your comments? It doesn't make your comments sound more right, only less well considered.

Trivial in the mathematical sense. As in, there is a trivial counter-example to your point. Citing infinity is a 'trivial' case. I'm using 'trivial' to describe my counter-example, not his error.

If I may be frank: don't use language in the mathematical sense if you are not in a maths classroom or you dont' know maths.

A small amount of the right data is better than lots of the wrong data. Collecting a lot of some data, because it's easy to collect isn't very helpful if it turns out to be the wrong data.

It would likely be more informative to instrument a few cars with some advanced sensor package and let well ranked drivers drive them around than to try to gather data from smartphones in existing cars, but I suppose it depends on what the end use is.

This is mapping data, not driving data. You need both.

More data is necessary with current technoloogy, in the sense that modern statistical machine learning algorithms are very bad at generalising to unseen data, and the only way to overcome this is to give them more examples.

There are machine learning techniques that generalise well from few data, but they are not very well known in the industry.

Also, though more speculatively, I think the idea of "lots of data" is attractive to marketing departments. There's something about algorithms that need huge amounts of data and compute, that only a select few companies can use. I guess it gives bragging rights, of a sort: "we got the biggest data around. Buy our stuff!".

But like I say, that's speculation on my part.

> More data is necessary with current technoloogy, in the sense that modern statistical machine learning algorithms are very bad at generalising to unseen data, and the only way to overcome this is to give them more examples.

Precisely.

> There are machine learning techniques that generalise well from few data, but they are not very well known in the industry.

Sure, and we'd all love to be using those. But even if you generalize well from small datasets, you still generalize better from larger ones.

> Also, though more speculatively, I think the idea of "lots of data" is attractive to marketing departments. There's something about algorithms that need huge amounts of data and compute, that only a select few companies can use. I guess it gives bragging rights, of a sort: "we got the biggest data around. Buy our stuff!".

It may be attractive to marketing departments, but it is also essential to data science projects like this.

>> Sure, and we'd all love to be using those. But even if you generalize well from small datasets, you still generalize better from larger ones.

Not to my knowledge. What techniques did you have in mind that work like that?

Literally all of them? Linear regression, neural networks, KNN, I could just enumerate all ML methods here, but I think the foregoing is sufficient.

I'm sorry, I don't understand. Which of the above generalises well from small datasets?

Who said they do? I said they generalize better from larger datasets. The entire point of this discussion is that more data is better.

I was referring to this part of our exchange:

ME: There are machine learning techniques that generalise well from few data, but they are not very well known in the industry.

YOU: Sure, and we'd all love to be using those. But even if you generalize well from small datasets, you still generalize better from larger ones.

That is not how those techniques work to my knowledge, so I was asking which ones you had in mind.

My point is that they all generalize better from larger datasets. Size is relative and some techniques work better with more or less data. Linear regression, for instance, can work quite well with much less data than a neural net. It just depends on the complexity of the problem.

>> My point is that they all generalize better from larger datasets.

Like I say, this is not the case. There are learning algorithms that generalise so well from few data that their performance can improve only marginally with increasing amounts of data, or not at all.

I appreciate that you probably have no idea what I'm talking about. I certainly don't mean linear regression.

> Like I say, this is not the case. There are learning algorithms that generalise so well from few data that their performance can improve only marginally with increasing amounts of data, or not at all.

Erm, no. Not unless they are solving the problem perfectly.

> I appreciate that you probably have no idea what I'm talking about. I certainly don't mean linear regression.

I work in the field. I'm quite certain i'm familiar with whatever it is that you think you're talking about.

The category of algorithms that attempt to learn things from few examples is called 'One shot learning'. It's usually in the context of image classification, but it applies equally well elsewhere. These algorithms still learns better from more data.

Do feel free to share an example of an algorithm that generalizes better from less data. I'll wait.

>> Erm, no. Not unless they are solving the problem perfectly.

Well, yes, that's what I mean.

I gave an example here a while ago, of how a Meta-Interpretive Learning algorithm, Metagol, can learn the aⁿbⁿ grammar perfectly from 4 positive examples:

https://news.ycombinator.com/item?id=17837055

That's typical of Metagol, as well as other algorithms in Inductive Logic Programming, the broader sub-field of machine learning that MIL belongs to.

>> Do feel free to share an example of an algorithm that generalizes better from less data. I'll wait.

To clarify, my claim is that there are algorithms that learn adquately from few data and therefore don't "need" more data. Not that less data is better.

That said, there are theoretical results that suggest that a larger hypothesis space increases the chance of the learner overfitting to noise. So what is really needed in order to improve generalisation is not more data, but more relevant data. Then again, that is the subject of my current PhD so I might just be interpreting everything through the lens of my research (as is typical for PhD students).

You work in the field? What do you do?

More data is not always better, it can be for sure, you need to have the analytical capabilities to turn it into useful information. Otherwise it's just hoarding.

They have those capabilities.

Bear with me here, trying to come at this from the other end.

Why do Uber and Lyft have to develop their own self driving tech? If what they sell is their routing algorithm for drivers, they can simply lease self driving cars from the companies directly and run them on their network. The car companies would be happy to get a cut of the profits.

What advantage would they get by developing their own tech? They have to manufacture the cars themselves or lease the tech to other companies to cover the costs.

Uber and more so Lyft seem late to the game and Uber in particular seem to be fraught with problems and have not made the progress they hoped. So why pursue that? I understand if they started early and are making great progress. On the flip side is the tech no where near completion? Then that would make sense. But Google seems to indicate that they are very very close to launching their own taxi program.

I'm gonna have to side with you here, I don't see the point of what they're doing. Except for distinction and marketing. Distinction, as in, if you have total control of how your car, it is easier to create functionality that competitors don't necessarily have. Marketing, as in, marketing.

Uber and Lyft have very little moat to protect them if someone else develops self-driving cars first.

They haven't succeeded in their initial goal of exterminating the existing cab companies, and that leaves them in a situation where developing self-driving technology could be a matter of life and death for them. The incumbent cab companies are much better positioned to profit from self-driving cars. They've already got all the infrastructure, expertise and staff it takes to manage fleets of vehicles. The bigger ones already have their own apps (e.g., Curb). If self-driving cars hit the general market first, basically all they have to do to run Uber and Lift out of town is buy some and factor the fact that they aren't paying cab drivers anymore into their prices.

By contrast, most of Uber and Lyft's basic business models right now are predicated on the idea of hiring humans, who in turn manage their own cars. That has deep implications: It means they have zero expertise in fleet management. They don't have any of their own garages for storing cars and handling maintenance. They do have apps, but the most valuable parts of their apps are the parts for managing humans. Incentive mechanisms designed to try and ensure that supply of rides and demand for rides remain relatively stable, for example. Their existing business operations are heavily built around recruiting humans - both riders and drivers. None of that is particularly useful in a world with self-driving cars, and the bits that are - basically just the mechanics of enabling people to hail rides and pay for them - are drastically easy to copy.

The first self-driving car deployment could be someone who buys a single car and uses Uber and Lyft as pre-made customer-finding platforms. If self-driving cars become cheaper than humans by enough of a margin for cab companies, then quick movers with capital can work with Uber or Lyft to deploy a self-driving micro-fleet of their own. It would be a great way to park your money, a much cheaper version of buying lease properties or fast food franchises.

Tesla has stated that this is exactly what they would allow customers to do, rent out their self driving cars when not in use to generate revenue for the customer and I assume Tesla would take a cut as well.

But not, I think, cheaper than a cab company setting up their own fleet.

Being a bigger player, a cab company is better-positioned to take advantage of what few economies of scale exist in this space: The power to negotiate a lower per-unit cost, and the ability to operate your own garage so you don't have to pay retail prices for maintenance and repairs.

The cab company would also be the more vertically integrated option, which gives them additional opportunities to economise and pass the savings on to the customer.

This seems mostly right.

That said, worth noting that the fleet management/maintenance expertise will be of unknown/unproven value for managing self-driving cars. No domain experience; just spitballing...

- Unmanned SDC might have significantly different refuel/recharge/cleaning/roadside assistance/accident-response needs. - Unmanned SDC availability/hours/mileage patterns are likely to differ significantly over time when they aren't tethered to bodily functions. - The size and location of fixed fleet-management infrastructure may be suboptimal for different patterns; they might start off as an advantage but become a drag as players with less inertia optimize. - SDC sensor maintenance is going to be a big unknown to these companies. - Their liquidity and vehicle retirement/acquisition processes may not be compatible with rapid fleet replacement without outside help. - Even if they do manage a rapid replacement cycle, the throughput of their fleet management processes may be closely tied to their normal acquisition cycles. - Unless they're going to choose that moment to transition to an app-only-ride-request model, they may have to overcome significant design/software/hardware dev challenges of their own to handle non-app customers (i.e., being hailed on the street, taking verbal directions from people who may be drunk or not know where they're going, stop requests, destination changes...).

Waymo has Avis handling their fleet management. The rental car companies are in a good position to take on Uber/Lyft if they get self driving. They're already renting cars; this is just shorter term rentals.

I can also see big automakers helping their dealerships convert into a network of fleet managers.

I think you are very wrong. Uber and Lyft are better positioned than the taxi companies. They are bigger, have more money, and could even let other people buy the cars, and let them run them. Taxi's have nothing but people, which is something you don't need, and old cars.

I imagine Uber+Lyft don't consider their driver routing algorithm a significant barrier to entry to the self-driving car market.

They likely expect the first company to come up with true self driving cars to start their own competitive service to Uber+Lyft. To them, it's an existential threat to not invest in their own self driving technology.

I think every self-driving car project is under the impression that the first stable and safe self driving car network is going to have no incentive to lease the technology out.

Yeah, making an Uber-or-Lyft-like service is easy. Lots of people have done it. Uber and Lyft price-competed those services into irrelevance, but if another service has self-driving cars and Uber-or-Lyft don't, then they won't be able to price-compete effectively.

I think it's likely that if Uber-or-Lyft don't get a sustainable technological advantage with driverless cars, they will actually stick around and make a deal with the company that provides the driverless cars, but they will have a weak negotiating position and will have to accept a small percentage of the profit which will not justify their current valuations (especially Uber, which has a much higher valuation than Lyft).

> What advantage would they get by developing their own tech?

Same with every company in the current climate. Develop every damn thing yourself for fear of anyone having financial power over you.

It really is tiring. Yet, I can't blame them too much. This is where I wish the government would step it. What do I care which company has the best self driving AI? Ffs, define best? It's likely the safest. So we're literally letting them compete for safety - knowing that some will be less safe than others.

There should be a government program here to share knowledge, license startups and ensure equal progression for the benefit of public safety.

I really hate that our safety is being handled by Uber of all companies. Especially since they've already killed someone.

The government currently lets anyone drive until proven unsafe. Self-driving initiatives are really only adding a few "drivers" to the road. Uber acted like a drunk teenager and they are off the road now. This system works, but with occasionally horrific humane toll. It's called capitalism

It's amusing that the California DMV autonomous vehicle testing rules Uber bitched about are very similar to those for a learner's permit:

- Must have a licensed driver in the car.

- Must report accidents.

- No driving for hire.

- No heavy trucks.

It's not the only capitalistic way to do it. You could have private companies developing the technology but a government testing program to demonstrate some level of safety.

That is what they do with impact crash testing. Private companies design cars, and then the government crashes some to see how they do in certain scenarios.

But, the US government isn't taking this route right now, probably because they believe that SDCs have the promise of reducing traffic fatalities over the long term, so they don't want regulation to delay the process. But that's a policy decision based on calculated risk, not a necessity of a capitalist system.

> That is what they do with impact crash testing

Eventually

https://en.wikipedia.org/wiki/Unsafe_at_Any_Speed

I stand by my assertion that this is just how capitalism works

> The government currently lets anyone drive until proven unsafe

I mean, sort of - you can't drive unless you pass a test of safety. I'm not sure exactly how that fits your model of "until proven unsafe".

Driving without a drivers safety course having passed, ie a drivers license is a serious offense.

> I'm not sure exactly how that fits your model

It only takes a written test to get a permit. You don't have to demonstrate safety, only knowledge of the rules of the road. You also need an adult over 21 years old to sit in the passenger seat. It is the lowest of bars

Sure but that's not a permanent thing, and frankly that is comparable to the current testing model of cars.

Furthermore, humans are not crafted beings. We can't cooperatively construct the knowledge of a young driver. Why is the terrible human bar our basis for success? Especially since this bar is only so low so that we can allow companies to compete? To have financial freedom to kill people?

My argument is that public safety does not need to be driven by financial success of companies. We've got the chance here to share knowledge for the common good. Yet, we're holding onto it so that companies can have the chance to make money.

Furthermore, why is "the lowest of bars" (in your words) our metric for success here? Usually the lowest of bars is a bad thing.

Humans are terrible abysmal drivers. I want them off of the road asap. Yet, I don't think hoarding public safety related data for financial success is the best effort towards achieving our goals.

From what I can tell, all of the other self-driving companies plan to launch a taxi service first. Even Tesla has plans for that, as evidenced by their "no lyft/uber self-driving" clause in their TOS for any Tesla you buy.

I didnt know this. I see plenty of teslas on uber.

Only for the hypothetical self driving mode.

And yet again head over heels. Most Teslas made today won't even be around to drive when SDC becomes a reality.

> they can simply lease self driving cars from the companies directly

Nobody really knows what company will develop truly practical SDC tech first. Therefore nobody really knows what revenue model that company will have.

If a vehicle manufacturer does it first, then probably their main goal is still to sell equipment, so they would probably be open to selling/leasing it to anyone and everyone.

If a tech company gets there first (or any company without an existing revenue stream), there's always a chance this company will want it all for themselves.

Sure, Uber and Lyft have the name recognition. And they have the active users (accounts created, apps installed, payment methods on file). But SDC is such a killer tech that whoever has it might decide it outweighs everything else and they can build their own app and get the users to come over.

If you're Uber/Lyft and this happens, you could be completely left out. Not only is your service less sexy, you're also paying labor costs that your SDC competitor isn't.

"Nobody really knows what company will develop truly practical SDC tech first."

No, bu the ones with all the patents will be Waymo, Uber, GM.. Lyft?

How does this work? Seems like Waymo would have been able to pick up enough of the obvious ones to kill competition for 20 years. Or just MAD between the big players?

> No, bu the ones with all the patents will be Waymo, Uber, GM.. Lyft?

Considering it looks like were at least 10 years away from having a self driving car industry those patents aren't going to have a long shelf life.

> SDC is such a killer tech that whoever has it might decide it outweighs everything else and they can build their own app and get the users to come over

I'm skeptical. This involves a tremendous capital outlay. Even if you solve that problem, you have to get the production lines running, logistical networks humming, and marketing gears going. Much simpler to buy or partner with one of the companies who have spent years training consumers to press a button and unquestioningly get into a car.

Masayoshi Son is happy to lend you billions of dollars if you have a working SDC implementation.

> billions of dollars

Production isn’t simply a throw-money-at-it problem. And in any case, if you can get billions to build you can get billions to buy.

Mr Musk would like to have a word with you.

> Mr Musk would like to have a word with you

Tesla had much more than massive capital availability. If anything, it spent most of its life at a significant capital deficiency relative to the competition.

For Tesla, there was nothing to buy. The only option was to build.

What incentive does a car company have to lend them the technology vs running self driving taxis themselves?

By that logic why not run their own taxi and car rental companies today? Vertical integration can make sense but it also often doesn’t.

I doubt Lyft wants to sit around and let Uber develop self-driving tech, since I doubt Uber would lease that tech to Lyft on very favorable terms.

I doubt Uber wants to sit around and let Lyft develop self-driving tech, since I doubt Lyft would lease that tech to Uber on very favorable terms.

This is a silly game. If self driving tech can even work (questionable in the next few decades), it’ll immediately become a commodity. No such tech could be protected for long. There’s not going to be huge profits to be made once multiple players have self driving fleets on the road. And since unattended cars will inevitably become trashed, individuals will just buy their own self driving cars, and traffic will get that much worse.

I can't imagine there won't be huge patent fights. Whatever is needed to make self driving work is clearly non-obvious or it would be done by now.

It may be the case of just throw enough ideas together and that works, in which case it may be effectively a commodity; everybody working on it has enough patents that cross-licensing works. Or it might be there's one key idea that makes everything work, and whoever files first controls it for whatever a patent lasts these days. Kind of depends which company owns that patent how it goes.

I have just read at https://www.lemberglaw.com/self-driving-autonomous-car-accid... a little bit about this topic. I think it's always interesting to talk about this future technology. However, for now, I wouldn't risk my family to ride inside one of these self-driving cars. They haven't 100% perfect/safe. Maybe 10 more years.

You are right. There is no good reason for them to develop and build autonomous cars, just like there is no good reason for them to develop and build conventional cars. When autonomous cars take off, Lyft and Uber can still use their current model. The only difference would be that the car owner doesn't have to drive.

I thought the big selling point for Lyft for awhile is that there profitability wasn't dependent on shifting to autonomous vehicles, where Uber's was. This seemed to make drivers move to Lyft because they felt they had more job security. Was that just perceived?

I’ve never heard anything like that. As far as I know, the only difference between Uber and Lyft is in strategy (Uber being much more aggressive about expansion). Regardless, it’s likely Uber would be profitable right now, even without self driving cars, if they stopped expanding into new markets.

That seems like a highly questionable statement demanding some evidence. Perhaps you meant if they retrenched into existing markets while raising prices and laying off all of their business development staff?

Uber definitely has the levers to become profitable anytime they want since they are entrenched in hundreds of major cities in the world. At the end of the day they control the supply and demand of their own platform and most Uber users will pay whatever the price is since they have been a necessity in many of their user's lives.

>most Uber users will pay whatever the price

So Uber is charging their current rates as a way of giving back to the community or whatever? If Uber (or even Uber and Lyft) were to double their prices tomorrow there would most certainly be a drop in volume. Just a couple of days ago I took a Lyft from the airport for no other reason than that a cab would cost about 2x. Yes, there are also circumstances and places in which Uber/Lyft is demonstrably better than a cab but the decision can also just be about who is cheapest.

Uber is funded by the Kingdom of Saudi Arabia.[1] Softbank's Vision Fund is mostly a front for the Kingdom. That may be more of a political move than a financial one.

Even that wasn't enough money. Uber recently borrowed another $2 billion at 7.5 to 8% for 5-8 years.

[1] https://www.crunchbase.com/organization/uber/funding_rounds/...

They don't have to raise prices. Just cut superfluous headcount by 70%. Instant profitability.

See [1]: "we can turn the knobs to get this business even on a full basis profitable, but you would sacrifice growth and sacrifice innovation" and "He said that it’s Uber’s commitment to 'developing' markets that are dragging things down, but he views that as an 'optional investment'". I'm not and have never been an Uber employee, but I've heard very similar things from people who are. Not great evidence, but hopefully that explains my opinion.

[1] https://techcrunch.com/2018/02/14/uber-could-be-profitable-i...

This focus on detailed mapping is misplaced. Most driving problems come from moving obstacles.

Was that the focus of Blue Vision Lab?

It's not like everything stops moving on the road when self-driving car cameras start scanning. "Mapping" isn't just establishing fixed elements, but also learning how things move and obstruct each other.

If I were Lyft I would be looking for an automaker acquisition. Specifically one with an EV program or willing to start one. I say this because once any automaker figures out self driving on an ev, the fleet rideshare aspect would be a negligible effort to add. Once that does happen Lyft would basically no longer need to exist. Maybe this move is what they are trying to do which is be acquired by Ford. Either I applaud there effort and look forward to what they achieve.

Lyft is a 15B+ company. Neither Ford, nor any other auto company, has the cash to acquire them.

Toyota has some 150 bn USD in cash reserves.

Toyota has the cash but seems to have trouble understanding that electric is the future of automotive transportation. I hope they figure it out for their sake.

If you’re not demoing your car at selfracingcars.com, you don’t exist.

So they have 1 car and literally coming in last in the AV field. This is a headfake for IPO. Waymo is 10 years ahead of them, minimum.

If Waymo's cars don't have usability issues and launch this year, Lyft's valuation will plummet.

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