Waymo and Daimler Trucks partner on the development of fully autonomous trucks
blog.waymo.com
blog.waymo.com
1. Prioritizing safety over everything has kept regulators far away and there haven't been any mishaps that I'm aware of.
2. Using multiple high resolution sensor modalities (LIDAR, camera) has led to very accurate localization and environment understanding, evident in on board videos [1]
3. LIDAR costs continue to drop precipitously, it now seems quite possible that next gen Teslas may sneak a unit on board to improve localization and depth perception.
[1] https://www.youtube.com/watch?v=ZsfUmU30kWY, https://www.youtube.com/watch?v=qAZ6tJSj9T4&t
For tesla's, they have in the past struggled with stopped objects - so you have a tesla going straight into something at 70 mph on autopilot (highway dividers, stopped trucks, crossing trucks). They also suffer from phantom braking which is very disruptive, and the fix may have been to basically to disable emergency breaking in these areas which increases risks if something is stopped there. Lider, even lower rest, would really help in all of this plus pedestrian detection for emergency breaking there (see China videos of teslas crushing mocked-up walkers in videos there).
Google also is building absolutely nuts training data for wide stereo vision recognition if they wanted. They are driving these vehicles generating very hi def point clouds with synchronized video. In terms of a training data set for a vision based system, this is basically a dream situation. Ie, if google wanted to reduce lidar reliance, they may be well positioned for that as well.
Finally, what people want is level 5 autonomous driving. What trucking companies want is level 5. So they are skipping some of the earlier levels but getting to where I think people want this to go. Could a trucking company put a lot right near freeway and waymo get's the truck from lot to lot (1000's of miles) with then last mile hand driven? The savings would be huge there.
They are going to be in a great position when this all shakes out.
Maybe I have missed it, but has there been any substantial use of Waymo outside of the Phoenix area? There is a reason why both Uber and Google were targeting that area for testing. It is an area with lots of sun, little precipitation, lots of relatively new, big, and straight roads, no real wildlife that might jump out into the road, and that is notoriously hostile to pedestrians. Is demonstrating a self driving vehicle is safe there truly indicative that it will be safe in the back alleys of Boston's North End when there is a foot of snow on the ground?
It just works out better for the company since these drivers know their city and that is all they do. It keeps the company from having accident insurance claims.
I doubt there will ever be self driving Tractor Trailer type vehicles in any city. It requires some serious skill and intuition about your rig and roads, like where to take the truck wide to complete a right turn without taking down the street light or pedestrian waiting to cross would be a good example.
I'm actually convinced that maneuvering a large, articulated vehicle is the comparatively easy part.
These two videos convinced me AIs can be pretty good at it: https://www.youtube.com/watch?v=HR4MEh5-paA https://www.youtube.com/watch?v=BYPA4ajTQgk
AIs might even have an advantage over humans because they can more easily use cameras on all sides of the truck to maintain awareness of everything going on around them. The AI should be able to predict with good accuracy where each part of the truck will be as the motion progresses because it can do the math, whereas a human has to rely on experience.
To me, the real difficulty for AIs is in the higher reasoning, like how you deal with situations (lane closed, vehicle traveling the wrong way, etc.).
[1] - https://www.dmv.ca.gov/portal/vehicle-industry-services/auto...
https://venturebeat.com/2020/01/06/waymos-autonomous-cars-ha...
Either way, 20 million miles is once again a drop in the bucket compared to the number which would indicate these cars are as safe or safer than human drivers. For comparison, Tesla is likely around 4 billion miles with Autopilot. I still wouldn't be comfortable saying definitively that Autopilot is as safe or safer than humans.
If you have a very limited fleet of self driving cars then maybe it's actually ok if they are not as safe as human drivers as long as the trajectory is towards better safety. I'm sure the very first test car driving anywhere was less safe than a human driver. Each step will lay the foundation baseline for the next step. The Tesla 4 billion miles can't be compared with anything since those aren't really autonomous driving. It's like saying there's a trillion miles on cruise control.
Waymo has 20 million miles using multi-modal sensory data: cameras, LIDARs, etc. Because the sensory data is gathered in multiple modes, it can be cross-referenced and calibrated against each other even if the placement of individual components change (so long as not all components change at the same time), meaning that the data can be used across multiple models.
Tesla has 4 billion miles using a single mode of sensory data, but because it uses a single mode of input (visual), old data becomes mostly useless when they make any changes to the system, such as to the visual resolution of the cameras, or their positioning on the vehicles. This is one of the big reasons that they get so many regressions with Autopilot.
Also, despite having 4 billion miles of sensory data, that hasn't stopped Tesla from being the industry leader in self-driving fatalities. The rest of the industry combined has only a single fatality.
You are missing the point. Tesla is the industry leader in fatalities primarily because they have such a huge lead in the number of miles. Miles are not directly comparable to each other because each company has a different approach, but Tesla's fatality rate is 1 every 800 million miles. Waymo appears to be the leader among the rest of the industry with 20 million miles. How can you predict Waymo is safer than Tesla at this point? They may end up being safer, but it is way too early to say that right now.
And that problem gets worse with increasing data, not better, because the useless data will drown out the important data.
Tesla's own legal filings shows that Autopilot is 2-3x more likely to get into accidents that Teslas without Autopilot engaged. (The 2x for Autopilot disabled but other advanced driving features engaged; 3x for all advanced driving functions disabled.) https://www.forbes.com/sites/bradtempleton/2020/10/28/new-te...
Their marketing claims are just that: marketing.
Where is the source for this? It wasn't anywhere on the article that you linked.
It’s clearer by hours instead of by miles. If a normal car gets into an accident every 1 zillion hours, and I’ve driven my research car for 20 minutes and then had it parked for 100 zillion hours and it only had one accident accident, I don’t get to claim it’s 100x safer. (100x fewer accidents per hour!) It has to be apples to apples.
Second, you may be moving the goalposts. If the car can drive in Boston, but not in, say, French Guyana, would you still say it's not true self-driving?
You just described a quarter of the US.
If it works for the full spectrum of usage for normal people, I’ll count it, even if it’s not all normal people everywhere. If we get something that works everywhere in the US, but not the crowded streets in India, you’d count it, right?
In a safety critical system, I don't like hearing "we don't need x, we can get by with just y". I like redundant systems, especially when one may do better in certain conditions than the other.
Now I am not so sure anymore.
Looking at the beta UI in the car while it is driving, I am impressed with how well it detects everything. There are examples in dark and even areas with leafs covering parts of an unmarked road. It doesn't look at all like the detection system is struggling.
The only thing is, that things seem to "float" a bit, or cars and pedestrians disappears when going behind other cars and reappears again on the other side. The sides of the road also seems to float/shift a little bit from time to time.
I wonder if they are not using a temporal filter of some sort (like assume a car continues in the same trajectory when goes behind another object), or if the UI only displays an "unfiltered" value.
In any case, I still haven't seen examples where the cameras didn't pick up anything relevant/critical.
> Hey, Elon, a question on LIDAR. If LIDAR were totally free, would you want to use it in your cars near term? Would that tech significantly help Tesla on the training of your neural network for FSD?
> I mean totally free, well, I think probably — I think even if it was free, we wouldn't put it on.
https://www.fool.com/earnings/call-transcripts/2020/10/22/te...
If it turns out Tesla can drop in a cheap LIDAR system down then the line then aren't they the ones that made the right choice?
Waymo taxi looks incredible but how can they scale it.
1. LiDAR is expensive - this makes sense from his perspective because he has already declared all Tesla cars on the road are capable of “full” self driving with their current hardware. If he walks back on his, he would have to retrofit a million cars.
2. Humans can drive with just vision, so why can’t computers? This is a naive argument because human vision isn’t just vision, it’s linked to a ridiculous intelligence in the form of human brain. And computers are nowhere close to replicating it.
If you want to read more on LiDAR’s importance, read some of Brad Templeton’s writings.
https://www.templetons.com/brad/robocars/cameras-lasers.html
https://www.forbes.com/sites/bradtempleton/2019/05/06/elon-m...
So both sides of that sentence seem wrong.
EDIT: it's also worth noting that Tesla is accounting for a whole bunch of future revenue from FSD. If Tesla admits at some point that they can't make it with the current sensors that would have huge financial implications.
Also, I don't think Tesla is known for their AI expertise like Google. So it remains to be seen if they can pull it off.
You have to think at human level in corner cases, like seeing an object fly into view and judge whether someone is likely to be chasing it into the street, which depends on knowing what the object is and how it fits into people’s lives.
To cut corners and costs.
Point #2 is deeply flawed.
Source: I have blind friends.
Agreed this is very naive, it implies that humans are good drivers. Humans are awful drivers, and it sure doesn't help that [human] field of view and depth perception is very poor [compared to alternatives such as LIDAR].
Step 2: Theory of mind.
Step 3: ???
Step 4: Profit.
So yes the numbers are high but the item that a car using vision would have similar problems is a bit dishonest and LIDAR only provides one other means to determine where something is.
However self driving cars have one major advantage that LIDAR and cameras don't solve. They are not easily distracted. They are also far easier to be restricted to the ability of the vehicle they are managing adjusting for weather and more. Oh you can fool them for sure and there are always edge cases.
Also thanks for the link shares, but I don't think either actually disproves Tesla's approach.
My understanding of the Tesla approach is: In order to truly 'solve' self-driving (situations on the road that have never been seen before to drive safely - think unannounced construction, collision or road closures due to protests), you MUST solve 'vision' with a very, very complicated and well trained neural net (re: ridiculous intelligence in the form of a human brain as you state). In addition, the existing road infrastructure (re: signs) is all built around human vision - and so being able to identify and interpret all of that is a requirement.
I find their approach compelling as in this instance where you have cameras with a particular neural net (which they are constantly refining the learning model on) that are training across the millions of cars across the billions of miles across the thousands of various edge cases into a generalized solution. You also have a re-enforcement loop via the nature of a human driver which 'intervenes' through the drive, a necessary step in refining the model at scale. Note: I am not saying that Tesla's FSD will be coming to a street near you anytime soon. BUT, I haven't heard or understood a well articulated argument that says 'lidar is really the only practical solution'.
This also doesn't factor in that I've heard Lidar doesn't work well in any kind of precipitation (light being refracted away from the sensor). Also, full self driving doesn't mean it can drive in situations that humans WOULDN'T be comfortable with (i.e. snowy blizzard or thick fog) so in either solution shouldn't be factored in as a part of the required solution set.
And finally, the practical threshold on a FSD system that would pass regulatory approval is evidence/data that it is materially safer than a 'typical' human driver. It seems the 'throw millions of cars and billions/trillions of miles at it' with a refined tagging system that approaches the narrow vision solution for driving forward in 'driveable space' seems to be most likely to reach a solution first.
Yeah this is dubious. You need to solve situational awareness. Vision is one way of doing this. Lidar is another, and lidar avoids many of the drawbacks of vision (having to do accurate world modeling based on cameras).
Tesla doesn't (and would be stupid to) feed camera data directly into a neural network. They feed multiple cameras into a complex system that involves both classical object positioning and neural networks to build a model of their surroundings. Then a downstream system consumes that model and makes decisions.
Its not a single end to end black box. Such an approach would be computationally infeasible, not to mention over-parameterized to all hell. No one does this, not Waymo and not Tesla.
While cameras are good at certain tasks (like detecting traffic lights), they are not good at all tasks, and using more specialized hardware for object detection and world modeling means that lidar based systems are strictly better. They have more information than camera based ones.
Tesla is betting on, somehow, making some breakthrough in computer vision that no one else can replicate, and further that lidar can't do what cameras can.
Your argument appears to be that since Tesla has more data, they'll achieve some eventual success, but the point is that they'd achieve more success faster with lidar, and everyone in CV seems to agree that we'd need pretty fundamental improvements in CV (and perhaps in cameras) before you get the same performance out of CV that you get out of lidar. That means that Tesla's betting on a less accurate world model being good enough. Maybe they're right, but so far we have some evidence to suggest that cameras alone have some pretty fatal shortcomings, and no evidence that Tesla has solved them.
The models they use are incredibly opaque, complex, and work based on statistical inferences that are built into the data. Making fundamental changes like assumptions about where that data came from is not trivial. Even more so given that we're talking about safety-critical applications.
This has been demonstrated many times by Teslas crashing into stationary or large obstacle in front of the cars that would have been immediately apparent to a human driver or a car with LIDAR.
That's why everyone else in the industry use both types of sensors.
People are actually terrible at driving, even when paying attention.
I really hope that the future of self driving reduces the road toll to a teeny fraction of its current level.
1. They are 10s of billions deep in a hole and make no profit (or revenue) and will not for multiple years. To ever pay back that investment they basically need to become Uber. And to get there, they need many more 10s of billions of capx. Uber is not even making a profit yet.
2. Waymo approach is the classic 'solve the easiest problem first' mistake. They started and have driven most of their miles, in literally the easiest place to do AV in the world. They have created incredibly complex high resolution maps on one small and simple region in Arizona where the streets are wide, the weather is always nice, and the general upkeep of the infrastructure is done very well.
3. Reliance on hd mapping for localization is fragile approach, and it essentially requires you to continuously map the whole global road system to have a real generalized driving solution. When will they have cars that can just drive on roads they have never seen before, something that human drivers often do. Seems to me this is the wrong approach to take.
4. Waymo still needs highly specialized vehicles, even with lidar becoming cheaper, their whole stack is still a very high cost low volume stack. How they will compete against an Uber driver in a cheap mass produced car anytime soon is totally dubious to me.
Tesla clearly does not want Lidar. Elon Musk even said he wouldn't use Lidar if it was free. What they are actually doing is using next generation radar and likely next generation sonar as well. See: https://electrek.co/2020/10/22/tesla-4d-radar-twice-range-se...
I don't believe that. He ultimately has to be anti-LiDAR because putting LiDAR on every Tesla is simply not feasible nor compatible with his plan to mass gather training data, so he has to justify it somehow.
What you're missing is that it's all moot if your self-driving car does not have a 0% failure rate, or close enough to 0% that it rounds down to 0 accidents. As soon as you have real accidents, you are done. Look at Uber. The one death completely ended their self driving unit.
So yes, it may seem cool to just go with the flow and rush things, but I get the feeling that this will be like the tortoise and the hare. Eventually LiDAR will be cheap, and Tesla will be stuck with years of useless training data.
To me, the revolution that actually mattered the most was the cell phone supply chain revolution (if you can call it that). That is what allowed these companies to equip their vehicles with cheap cameras, radar, and eventually LiDAR.
> I don't believe that. He ultimately has to be anti-LiDAR because putting LiDAR on every Tesla is simply not feasible nor compatible with his plan to mass gather training data, so he has to justify it somehow.
Why is using Lidar not compatible with mass gather training data?
Also he recently said they wouldn't use Lidar even if it's free. He didn't leave the door open for when the price goes down sufficiently. He could be wrong obviously, but I don't think he is in denial.
https://www.tesla.com/support/full-self-driving-computer
I suppose they wouldn't have any problem asking for Lidar installation if that were to be required?
2. Solve the easiest problems first, as opposed to what? Even the easy problems are hard to solve, trying to solve everything at once is going to be floundering around with nothing to show. Is an automated vehicle that can only do the easy parts super useful? Not really, but it's somewhat useful. In a model with human controls, this is tractable, assuming there's a safe handover --- either directly with appropriate advance notification and acceptance, or by parking and switching; without a safe handover or human controls, it's pretty limiting, but some areas would have most of the year coverage, and some areas have tourist populations that overlap with fair weather, so it's something.
3. I don't think this is a big problem, if the vehicles are driving the routes, they can update the mapping. The road map is finite, and most places don't change that often. Again, safe handoff is critical, though.
4. Yep, it's expensive. But mass produced, less expensive lidar is plausible. Given this thing is still several years from mass deployment, constraining to today's inexpensive tech is too limiting. Figure out if you can do it, and figure out how you can do it with unlimited budget, and then figure out how to make a working system with less.
P.S. I can't believe I'm defending Waymo here. Usually I'm the staunch negative against automous vehicles. On the other hand, Waymo's serious errors have been more amusing than dangerous (let's try to merge into a bus at low speed, cause it'll certainly move out of out way)
If your mental model of how the world works is "Elon Musk always tells the truth" then this is compelling evidence, but if you have a different mental model I don't know why you would take it seriously.
The important point is that he is CEO and its well within his power to develop his own lidar system, or sign a contract for lidar. Or to do research on the topic. Yet he has not done that or shown any interest in that.
At some point you have to ask yourself, is somebody engadging in 4D chess over 10 years or is it simply his opinion.
2. Arizona doesn't always have nice weather. Haboobs, fog during the winter mornings/nights, monsoon season with heavy rain and hail. There are plenty of weather conditions in Arizona. And if building the visual recognition is the easy part, what is the hard part?
3. Fair, but that is already something their parent company does as another business; Maps/Earth. There is nothing to suggest they CAN'T do this on roads they haven't seen before, but it's obviously far safer to do this on roads they know.
4. They partnered with Daimler to make self driving trucks who couldn't use Uber to move shipments. They aren't competing with Uber necessarily.
As for Tesla, there is nothing to suggest they are doing it the right way. They have had numerous self driving failures.
They just raised $3bn (the vast majority of which is presumably not spent), and piecing together other sources indicates Google put in a couple billion of their own.
This article claims they've spent $3bn on R&D:
https://www.caranddriver.com/news/a30857661/autonomous-car-s...
Google's approach requires a lot of mapping, yes. But they mapped damn near every road in the important markets to pursue maps, an opportunity two orders of magnitude smaller than self driving.
If their cars and software are both ready, the mapping won't be more than a small roadbump.
3. Sure, they use maps.
But do we know that part of their algorithm there aren't map-free techniques ?
The challenge of autonomous trucks isn't that they're "more dangerous," it's a matter of physics and current LIDAR technology. The weight of the truck means there is a minimal safe stopping distance at a given speed. Frankly, the quality and distance of current LIDAR tech falls short of the distance required for safe stopping at the average highway speed for a truck.
Put another way, the autonomous driving stack has difficulty seeing far enough ahead of a truck to successfully stop in time to not cause an accident in highway environments. You'll need better fusion of perception stack (LIDAR + imaging + neural nets) or better LIDAR ranges to be able to deploy autonomous trucking sooner.
This year we've seen Luminar start to go public via SPAC, and their high-range Lidar has been mounted on most (all?) non-Waymo self-driving truck prototypes. There's even a public dataset with Luminar data now: https://github.com/TRI-ML/DDAD
So early on, when the number of people that know the system is small, it seems better to work on the bigger, more general problem, and branch out to the sub problem later on when there's more people to work on it.
From another perspective, of you stop work on cities to finish a trucks launch, you're losing ground to competitors working on cities, which may be decisive in the research phase.
- Insurance, since you're transporting others stuff at highway speeds
- You still need a CDL driver right now (not cheap)
- Economics - the failure of Starsky Robotics might offer some clues here
- Interstate Regulations on Driverless testing (?)
The interestion questions are "is the 'greater economic incentive for trucking' assumption correct in the first place" and if so, why were we still bothering with consumer automotive in the first place?
Teamsters would say otherwise.
That's obviously not a set of statistics that is possible to directly observe; such a conclusion is only possible through a model dependent on extrapolation which assumes that a relationship holds outside the observable range. (And it's also somewhat incoherent, as both corporate decision-makers and legislators are subsets of the public.)
It sounds like you're airing a general grievance against sampling, but I'm sure that's not what you mean, so please elaborate?
> And it's also somewhat incoherent, as both corporate decision-makers and legislators are subsets of the public.
I don't see how that renders anything 'incoherent'. Are you interpreting "unanimous public support" to mean "literally every single citizen supports this policy, including corporate decision-makers and legislators"? Such pedantry aside, it's entirely possible for an overwhelming majority of the public to want a particular policy decision irrespective of whether "corporate decision-makers" or legislators find themselves in the majority or minority.
In whatever case, the statistic I'm thinking of is from former labor secretary Robert Reich's "Inequality for All" documentary. I don't care to skim it for exact phrasing or numbers. Similar information can be found here: https://represent.us/action/no-the-problem/.
You obviously aren't going to have any measure that actually has 100% popular support. So the only way you could get to “if it has unanimity among the public, it has only a 30% chance of passing” based on anything even approximating empirical science is to take some observations about success at various less than unanimous levels, and develop a model of the relationship between public support and chance of passage, and use that model to extrapolate beyond the high endpoint of support from the data. Without a strong theoretical rationale which you have good reason other than the observed relationship within the tested range to believe is the explanation for the tested phenomenon, it is always suspect to extrapolate an empirical relationship outside of the range in which it has been observed.
> Similar information can be found here: > https://represent.us/action/no-the-problem/.
The study linked to support that is interesting, but since it models the effect of interest groups, including mass interest groups, separately from both public and elite individual preferences, and finds that both elite preferences and interest group alignment have significant independent impacts, but average citizen preferences don't have a strong separate effect, what it really indicates is that average citizens are effective in politics only to the extent that their preferences energize action through organized groups.
[1] https://www.truckinginfo.com/10126660/fmcsa-denies-request-f...
Trucks are big, heavy, and human-driven ones already scare enough people on the road. If you launch with trucks first, you risk a backlash from people who are afraid of them. If you start with an autonomous taxi service people can sit in the cars themselves, feel safe, see that there's a "safety driver" present, and even if they aren't in the vehicle they still get to interact with them on the roads. you can make people comfortable with the idea before you launch the trucks. and everything that goes into the self-driving taxis is still valuable development work for the trucks. I wouldn't be surprised if self-driving trucks actually are the first real commercial launch target for waymo, and the taxi thing is just PR.
as long as the regulatory situation allows it, yes. but until they're seeking approval to have them actually allowed on the roads, making people feel warm and fuzzy is pretty important.
Still have a driver do the last mile, for deliveries to stores, homes, etc, but the between cities, long haul routes seem ripe for automation. (especially with all the laws on driver mandatory rest periods).
You save a lot of money on accidents too by automating.
The problem is just that you still need a driver anyway, so it really doesn't help that much for now.
Its the same problem Waymo has in general.
And who will win general driving is yet open, and just assuming it Waymo is foolish. I think is funny how on HN people dismiss Tesla when there are actually tons of people who already do their commute on the highway almost without intervention literally all over the world.
They have trucks that will have this same feature and they have already talked about the idea of platooning.
Just downvoting me because you don't think Tesla approach is as good as Waymo/Daimler approach doesn't make what I said wrong.
That's just advanced driver assistance, nowhere near "full self driving". Granted, it's better than other cars, but it's still not self driving.
Also, "almost without intervention" has no meaning if you have to keep your hand at the wheel at all times ready to takeover.
Or I guess you mean they still need a driver because of compliance/legal issues?
What you suggest is an interesting idea, a total change on how we think about truck logistics. Its certainty possible but I don't know if we will ever get there. By the time the legal things are sorted out, the trucks might just drive themselves all the way to the destination.
If you look at the space, there's the Waymo approach - which is safety and comprehensibility first - and not very many companies taking this other than Cruise.
Instead, you have this pile of companies who are pursuing mostly-deep-learning solutions, including half a dozen startups of various levels of craziness and Tesla. That scheme is unlikely to actually result in a safe outcome.
Waymo figured that out very early on, and it's a lesson others may find out the hard way.
Source: am Google engineer
And then in another 2 years they'd deprecate your car and launch a new competing self driving bus initiative.
Then add to that it's had like ten billion dollars of investment. So it needs to turn into an uber sized company or it will be a failure. Not exactly standard Silicon Valley MVP style prove the business model, acquire customers, and then get series A investment.
Even if it can solve self driving, it could still fail as a company.
It's solving a hard problem and you don't build a Series A around an unsolved hard problem even when you know that there's market demand. That's why nobody is running a startup selling electricity below cost as an MVP for their fusion reactor back-of-a-napkin sketch.
If it's just a matter of aggressive driving, I'm sure Waymo has a dial they can turn that would make them go faster, and then Uber has already proven the market for them. But they need to get the safety margins up before doing that, which is a hard problem.
Is it the Silicon Valley model? Well it's not the get rich quick VC model. It's much more like the Bell Labs model of growing a transformative but immature technology in house until it's ready to deploy.
Waymo is just throwing money at the problem without a clear path to return on investment. We all know how patient Google is with that kind of project. It's asking to get the whole thing cancelled. It runs very much counter to the typical Silicon Valley model.
Waymo would be, what -- 25% or 30% of the price for maybe 1.2x the time? And with total privacy, which Uber Pool doesn't provide?
And heck, a Waymo Pool might be just 10-15% of the price. That would be revolutionary.
I can't imagine the market not demanding that. The tradeoffs are totally different from Uber Pool.
Not just this; time varies wildly. Maybe it's 1.5x, maybe it's 3.5x. You can't really plan around it.
Can they accomplish that before the project gets cancelled? Because at the rate they're burning money there is a finite time that alphabet will continue to back it.
I have repeatedly had Uber and Lyft pick a driver, I wait tens of minutes, and then the ride is cancelled. If Waymo can avoid that, then I would gladly pay MORE for it. (I waited at a hospital for 2 hours, at 2 am, one time.)
I've also had an Uber driver who had no idea how to drive. My wife had an Uber driver who was convinced the destination was a hundred yards into one of the Great Lakes, and couldn't be convinced to change their mind.
I think you're also underestimating the value of long-haul rides. If I can get a "moving hotel" that drives me over night to an awesome destination, I think that would be awesome. I mean, ideally it's a train. But that takes tons of infrastructure we don't have. If I could pay airline rates, and not have to go through airport security, not have delayed or cancelled flights, and could travel while sleeping? That would be phenomenal. Especially right now, avoiding airports and crowded planes? And post-covid, I think it could really help tourism.
I think you're also under-valuing having a self-driving car from the novelty aspect. Picture you're at a Disneyworld Resort, and you order Lighting McQueen to take you to the Magic Kingdom. He pulls up, it looks like him, sounds like him. You hop in, and you see his face on a display, continuing to talk to you.
Heck, picture the whole car is designed like a movie theater. Nice and dark, great sound.
Also, picture a self-driving car with a liquor license. That's like limo service, at Uber price, right? Hard to do that with an actual human driver, without a limousine license, right?
And we've all seen research that says that once you get enough self-driving cars, traffic jams go away. That alone is worth the cost, and then some.
And also higher up the thread
> I think Elon Musk makes some good points
I find it funny that all these "Good points" happen to come from the very people who have a vested interest in Waymo failing and their own alternative path being the better choice...
Only time will tell which path was better, and I'd honestly take the points from their competitors with a grain of salt.
However, I think you're right about the capital intensiveness of Waymo's approach. Not only do they have to spring for the capex (vs Tesla where they have conned^H^H^Hvinced their customers to front the capital for the fleet) but they have to operate a continuous remapping fleet to keep their hi-def maps up to date. Possible they could incorporate that part into the taxis, but it's still something Tesla doesn't have to do. In my mind, there are two possibilities: Waymo works and Tesla just never quite gets there, and thus Waymo has the time to gradually map and roll out to cities all over the world, or Tesla works too, and has much more attractive economics, and thus can deploy faster and owns the market, even if it takes it longer to achieve "true" FSD. Only time will tell.
I don't think that's the same thing. I don't like uber pool because it's very unpredictable, not just that it's slow. If it were predictably slow, I might use it more.
I'd pay a premium for this. Some uber drivers are frightening, and to me it's not worth it the clenching to arrive 20 seconds faster. For the driver though, it may me more rides in a day.
I get that with Waymo you are trading labor for recouping R&D, maintenance etc. which might still be expensive but I don't think the market has had the opportunity to decide on this type of trade-off yet.
Customers may be willing to trade off on ride speed if Waymo gives them a better user experience in terms ride quality and vehicle standardization (i.e. the roomy Waymo minivan).
How is that the same thing? Sharing a car with someone is definitely not the same as just being slower.
We can't even solve for "slowly put that trailer over there" with highly constrained environmental variables. There's no way we get commercially useful highway driving until after that.
Even in the U.S. truck drivers are not that expensive.
Around 5000 people die per year in crashes involving trucks in the US alone.
https://www.trucks.com/2018/05/22/shippers-truckers-soaring-...
> Driver compensation accounted for 33 percent of motor carrier expenses, according to 2016 data collected by the American Transportation Research Institute, or ATRI.
Marketing departments just have to ruin everything.
My wild ass guess is the extra compute power is running the robot driver code. Then the human and robot actions are compared. Do some AI magic to close the gap. Any big unexplained significant deltas are forwarded to the mothership, for further analysis.
This notion is why I've gone from bear to bullish on Tesla's autonomous driving. Tesla has more data than anyone else. And per Norvig, IIRC, more data is better than more compute.
What little I know about Tesla comes from HN peanut gallery and Munro Live and others on YouTube. This parallel simulation idea is just what I'd do. And may explain what all that spare compute power in each car is being used for. I don't know if my wild ass guess is original or not.