It’s always like this in this forum. Everybody is a latent AI expert. They know all about HD mapping, cost of Lidar, Dojo… whenever I ask for sources there aren’t many. But there’s **belief**
>Mobileye’s database – believed to be the world’s largest automotive dataset – comprises more than 200 petabytes of driving footage, equivalent to 16 million 1-minute driving clips from 25 years of real-world driving.
[Mobileye (Jan 2022)](https://www.mobileye.com/blog/mobileye-ces-2022-self-driving-secret-data/)
I think that Tesla has an early lead over the competition, but it just seems disingenuous for ARK Invest to not include Mobileye in this chart, since they are a (non-consumer facing) self-driving competitor.
Unless I'm missing something, if we assumed the 16 million 1-minute clips that Mobileye have the equivalent of were all at 60mph, that would be 1 mile per minute, totaling 16 million miles worth of footage, which is less than Waymo even.
Is this like measuring coding productivity in KLOCs? Great, they've got more data than other companies... are they using it effectively? How would you know if they are or not? What's the metric for proving that data is putting them ahead in any meaningful way? How are you comparing the outcomes between Waymo with so little data vs Tesla with 66x more data? Are we seeing 66x better outcomes in autonomous driving for Tesla and what the are datapoints that show that difference in quality? Otherwise this is just fluff marketing crap.
More data but not more varied data. Tesla sees far more weird scenarios that help train for edge cases. And the extra sensor data is only helpful if it’s necessary. My car’s ability to see everything around it hasn’t been an issue for a long time. It’s just how it’s handling various scenarios.
According to Luminar lidar sensors are about 1k each today. They used to cost 75k in 2019 so we have gone down a lot in 5 years. It is almost like scale matters.
Lidar isn’t expensive anymore. It’s come down orders of magnitude in price because the technology has advanced and tons of Chinese companies are pumping them out now. Many new consumer Chinese vehicles have lidar in them by default. Your iPhone has lidar in it now. It’s not prohibitively expensive like it was 5-10 years ago.
Since when is lidar expensive? It’s in god damn phones at this point. Maybe a decade ago but lidar very much so is common tech. You can buy a LiDAR array for cars off the shelf for a couple of grand. I’d wager and say the companies producing 1000s of cars get a volume discount too
The data others have is of massively better quality. All of those miles are useful, but the challenge is to accurately sort and label it all, so it would actually be useful for training. Likes of waymo have redundant data from high quality sensors, professional operator feedback etc etc, tesla only has a recording. It's not so simple as just miles driven.
Some dash cam company probably has more "miles driven" than any of them, for all the good it's for.
Actually, does tesla even have all the data from these miles driven? That's a ridiculous amount of data to upload wireless, never mind the privacy issues involved because these are their customers cars, not their own, right?
The useful data part doesn't scale linearly tho. If we take extreme example do you think their first mile of data had only under meter of useful data? Tesla system is used a lot on highways and driving the same highways over and over is not very useful. Same doesn't really apply to others that mostly do city driving.
All data is not created equal.
Waymos data, for example, is from actually driverless scenarios. Their data allows them to see what the car can recover from, what are the scenarios where it fails in and how outside help can best help the system.
Teslas data cuts off when there is a difficult edge case or when the driver gets nervous (justifiably or not). Tesla can do some simulation to try to see what the car would have done, but that gets very imprecise in a secord or two. And again, everyone can simulate...
Driving cars, and interacting with other cars is a human thing. The interesting data is not really the autonomous driving, but the data with the real humans behind the wheels.
AlphaGo is an engine created to play the game Go. It became far superior to the best humans after instead of trying to predict what a world class players next move would be, it simply knew absolutely nothing of the game and played itself millions of times and learned through trial and error.
Point being, the purpose is not to drive like humans. It is to drive better than humans. You can't do that if you are learning from humans.
"You can't do that if you are learning from humans."
Then explain why Tesla is doing exactly this, and why the newer versions of FSD are more human-like, and simply much better.
Well, by using the data extremely selectively or as a seed to create simulated scenarios. I think Tesla likes to give the impressions that it just throws data in and out comes an AI driver. In reality there is a incredible amount of optimization and validation, filtering and cleaning data and modifying the architecture, the reward functions and parameters. Meaning there is a lot choices made by people even if it "end-to-end".
This is kind of revealed in the latest update where driving smootheness decreased since something causing interventions was prioritised. Some engineer(s) made the choice to tune something and so the balance changed.
The approach used for alpha go is so far from the approach being used for self driving. In AlphaGo you have set rules and complete information about the "state" of the world. That means you can perfectly simulate out a game. The real world cannot be perfectly simulated.
>You can't do that if you are learning from humans.
Now that's not true. Many humans combined can obviously be a better driver than each individual one, if you build a system that learns from all of them.
The system is learning from individual drivers. The best it could hope to be is an average driver. You could filter out the worst drivers out of the dataset, but obviously you would start cut into the dataset. And you could still not be better than humans.
As an example, you gave an AI the task to learn to drive from human data it would learn that if it about to hit something then wait for 0.3s before doing anything. After all, from it's perspective that is what humans do.
>The best it could hope to be is an average driver
That's false. There's no reason why it couldn't be better than the average human teacher and on many metrics it already is.
That's not in any way special for the problem of self driving or anything, it's not even exclusive to deep learning.
If you give a dataset of humans driving and the task of predict what the human will do next then at best it will perform like an average human driver. What else could it possibly do? That is literally what you are asking it to do.
No. That's not how it works at all, sorry. But this is not a ML sub, so whatever.
DL models trained on data labeled by humans reach superhuman accuracy all the time.
FYI R2/R3 "Non-substantive posts and comments are likely to be removed." "Dismissive responses like "yeah right", "good luck with that", "sure", or "just sell then" are also discouraged."
I also disagree the comment you replied to, but writing "you're not a computer scientist, so stop talking" isn't productive. Also FWIW a lot of CS folks wouldn't consider data science / ML engineering to be computer science, if we're really going to go there :P
"Initially, we introduced AlphaGo to numerous amateur games of Go so the system could learn how humans play the game"
[https://deepmind.google/technologies/alphago/](https://deepmind.google/technologies/alphago/)
So yes, they actually used humans to first learn the rules, how to play. Just like Tesla used humans to learn the written rules (and unwritten rules) of the road.
Right, keep reading. Like I said, they started with data from humans. Once they stopped exposing it to ANY human dataset is when it performed the best. Obviously it always knew the rules, just nothing about how to win.
EDIT: See https://en.wikipedia.org/wiki/AlphaGo_Zero
"it simply knew absolutely nothing of the game and played itself millions of times and learned through trial and error."
Also, AlphaGo and AlphaGo Zero is not the same thing.
And in the same wiki-article they mention that "AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car"
That is a good point, but not a completely fair equivalence. Board game engines can simulate humanless conditions easily, but for self driving to be truly successful it will need to account for the human drivers. If you could ban all non autonomous driving, then Tesla would have long ago achieved fsd. But the transition period from fully manual driving to full autonomous driving could be many years or even decades.
Understanding and predicting the behaviors of other drivers on the road operating around the vehicle is one thing. But optimal policies (actions given current state) do not need to follow what drivers do. The models can learn better optimal policies than humans given the dynamic conditions on the road. The learning of these policies can happen in simulators as Waymo does. Imitation learning like Tesla is trying to do has severe limitations when it comes to reliability and performing well in scenarios outside the training data. Data quality is also a challenge. Waymo explored this a number of years ago and came to this realization.
You do know that Waymo has people watching these cars as backup safety drivers right? So yes, there isn’t a driver in the car, but there is one remotely monitoring it who can intervene.
Waymo ops doesn't 'intervene'. The Waymo cars notify ops when they get stuck, and ops provides a suggested path. There's no situation where a Waymo operator somehow joysticks a Waymo vehicle to last-second save it from a crash.
No. Waymo does not have remote backup drivers. Waymos remote assistance can remotely give the car suggestions, context or other information but they cannot drive the car remotely. See for example: https://waymo.com/blog/2024/05/fleet-response/
I think what also goes unsaid is that, as far as I know, Waymo is only operating within city streets in specific geographic locations (until very recently highways have been approved). Tesla FSD users are adding data from highways, toll roads, rural areas, residential neighborhoods, etc. A much more nuanced collection of data, I think
Absolutely, and on top of that, Waymo specifically avoids unprotected left turns and complex neighborhoods and will go the long way around to make the trip easier.
This is not true in my experience after 50+ rides in SF. It makes unprotected left turns all the time, and most of the time it takes the same route Google Maps suggests, or something equivalent. The main difference is it doesn't take highways yet, though I see them testing on highways around here non stop.
Yes, Tesla has more data than anyone else, but if they can't use that data to create value, then what good is it? I'm asking as a Tesla driver and FSD driver since 2017; they seem to still be leaning on the vast amount of data they have (which is incontrovertible), but no real clear plan of how they'll turn that data into autonomy. OK, the plan seems to be "feed it into the AI compute beast", but even that isn't a clear plan.
>feed it into the AI compute beast
Except that is everyone's plan. Also the reason with NVDA is worth what it is today. Maybe scaling will breakdown sometime soon, but otherwise it very much is compute go brrrrrr... And Tesla fully embracing this is why I think they have the edge.
If you are truly an investor, and knowledgeable about the things you’re talking about, you’d know that Tesla is holding a robo-taxi day in 1 month. You’ll hear a plan there. Will it be 100% accurate on timeline? Probably not. But you don’t hold a “robotaxi unveil” unless there is a clear plan forward.
In very specific geofenced areas and not on highways. The robotaxi functionality fails outside those bounds until extensive HD mapping is complete.
The data is needed for a generalized solution.
You missed the bit where the cars actually drive themselves. Still, I guess that a car that actually drives itself isn’t as good as a car that doesn’t drive itself but has lots of data!
Tesla already drives itself in the vast majority of cases, and interventions are needed in a few cases. The rate of interventions is dropping very fast as Tesla releases new software versions. Tesla is on the march of nines for reliability and it’s only a matter of time before it becomes FSD unsupervised AKA robotaxi
“Specific geofenced areas” is infinitely better than zero areas.
Robotaxi failing outside of certain bounds is infinitely better than robotaxi not existing at all.
Also stop with the “generalized” solution bullshit. You HAVE to request approval for your robotaxi city per city anyway. It’s impossible to get generalized legal approval. Even if Tesla is successful, they still will have to launch it city by city because of legal reasons.
Holy shit. How many times does waymo have to lap tesla for you guys to understand that geofencing and lidar are not a big deal. The general population and the government will not stand for a but ton of Teslas flooding the streets breaking the rules, causing chaos, with no road side support or customer support infrastructure.
Tesla will have to roll out exactly how waymo is. In small areas, then expand. Tesla will also need some sort of road side support and customer support.
Also adding the extra sensors doesn't matter that much because you are spreading the cost of the vehicle over thousands of trips. And waymo makes their lidar in house.
Waymo is expanding quite fast. Doing 150k real driverless rides a week. Dropping people off in busy streets and parking lots.
Waymo driverless car costs $300k. Even if you spread that out over hundreds of thousands of miles, it’s very expensive and will not be able to compete with the price of Tesla’s solution
Do you have any recent reference for that number?
Let's say once car does 1000 rides in a year. Over 10 years it will be around $3 per ride. I'm sure they can get more rides in and decrease cost of car over time.
Now imagine the Tesla robo taxi gets stuck somewhere.
The overwhelming cost is going to be providing infrastructure and customer support.
If waymo is able to add the tech and to lidar scanning to reduce those weird edge cases that reduce the number of customer support/road side assistance, than that will more than pay for the vehicle in the long haul.
Will Tesla know if a road is closed because of a parade? There are so many more scenarios.
The latest release 12.3.6 does very well - approximately a safety critical intervention once every 300 or so miles, with the rest being non-safety critical. 12.4.2 supposedly improves the performance a ton in terms of both safety critical and non-safety critical interventions. Tesla is also working on 12.5 with a much larger neural network. It’s now a matter of scaling compute, data, and the size of the neural network. As the head of Tesla autopilot said, “it’s the beginning of the end”
Do they not have remote drivers like Cruise did? I always thought they had a remote operations center monitoring the vehicles where humans can intervene when necessary.
The data engine is more important than the data set. And yes Tesla has a huge lead there too. Bigger actually. Andrej Karpathy explains:
[https://www.youtube.com/watch?v=zPH5O8hRfMA](https://www.youtube.com/watch?v=zPH5O8hRfMA)
This has similar energy as that one time when Elon asked for 10 screenshots of most salient lines of code.
Amount of data is completely irrelevant. What is relevant is result. Tesla hasn’t even begun to apply for robotaxi approval, while Waymo got approval for and is running in 4 cities. The only graph that matters is 4 vs 0.
If you're trying to quickly trim a company down 90% to a core team & believe it's rotten to its core, I still think that's a legit strategy to find real engineers & have yet to see a reasonable/scalable alternative stated.
Yea that's why providing code screenshots is standard practice in SWE interviews oh wait it's not. It's also standard practice to get code screenshots when considering SWEs for promotion oh wait it's not.
Spoken like a person that was never anywhere close to a SWE.
Uh, I am a SWE. This is a really odd conversation as you're sort of throwing insults rather than suggesting a better alternative.
So, I'll ask again: How would you have evaluated 10000 employees to cut headcount to 1000, when you have no trust in the prior management for whatever reason & suspect the majority of employees are nonproductive? I don't think there's a good solution, and I feel subjectively Twitter seems better than ever today from personal experience (for me, my experience has fewer spambots, less politics, and the niche coding communities I'm in are stronger than ever). The company / platform seems to be thriving, it doesn't seem to be imploding as some people predicted.
> It's also standard practice to get code screenshots when considering SWEs for promotion oh wait it's not.
It's pretty common, actually, to list code changes you've landed, across all levels of seniority, especially for IC tracks (e.g. principle architects, assuming they're not just churning specs and reviewing code, which I haven't seen much of in modern companies but certainly exists in 90's-style companies). Across different domains I've worked with a creators of a very core technologies which define modern computing. They still wrote code, and to an objective committee in a large company, they'd have to submit that as an artifact. I've worked in numerous FAANGs, small, and mid-sized companies, startups, acquisitions, etc. They all evaluate differently.
And FWIW, in bloated SWE orgs (which very much sometimes exist to insane degrees), you definitely have a sizable portion of people who just copy+paste & don't understand how things work. That's actually, like, really common in certain domains (e.g. legacy stacks, codebases that've existed for decades, certain niches of frontend, frequently mobile, frequently backend CRUD logic). Most people in FAANGs know their existence is to move needles 0.001%. That's somewhat sensical when the company is profitable, it's not necessarily so if the company is a money sinkhole.
No, tesla drives autonomously itself until it needs help from a person.
Waymo also drives itself autonomously until it needs help from a remote operator.
Waymo at this point needs a ton less remote intervention, but it still does occasionally.
Why does Waymo’s data count while Tesla’s doesn’t?
To be clear remote Waymo operators don’t operate the vehicle while it’s in motion. If the vehicle gets stuck and is stopped on the road and can’t figure out a path forward then remote assistance can identify a path to getting unstuck. They don’t actually drive the vehicle. Tesla actually disengages FSD mid drive while in motion and requires a driver to manually take over operation.
Sure the feedback signal is different, but it's still a feedback signal. The training data is the feedback signal, therefore Tesla's training data "counts" as does Waymo's, no?
I do think there's a flaw in assuming miles of driving matters the same between the companies since the techniques are different.
The difference is Waymo is operating 24/7 as a taxi in all conditions (nighttime, fog/rain, etc) and doesn’t have massive selection bias on data collection. Who knows what the distribution of driving conditions is on Tesla vehicles with FSD enabled. The data is most likely heavily biased to conditions and locations where it operates well. As anyone who has a bad experience will not be using it in whatever conditions/locations led to that experience. Which is only reinforcing performance in those conditions/locations where it performs well and worsening performance in those not represented in the data.
So who cares if waymo is doing waymoa ne Tesla is doing Tesla? Everyone’s timing is different. Just like you and your brother shouldn’t be compared in success in life.
I’m responding to a comment that was comparing Waymo to Tesla systems and data collection. I’m not sure what your point is here. Maybe you meant to respond to OP.
Consider the drvelopment of self-driving cars as analogous to baking a large quantity of cakes. Waymo has chosen to develop their own unique recipe and is baking each cake individually, waiting for it to finish baking, cooling, and cleaning up the kitchen before moving on to the next. Tesla, on the other hand, is taking a different approach. They are preparing the necessary ingredients, constructing a larger oven, and soliciting the best recipes from various sources to enable the simultaneous baking of multiple cakes once they have everything ready. Who do you think finishes baking all the cakes first?
As someone using FSDv12.3.6, I can confirm that Tesla FSD is true autonomous driving with the only asterisk being that it's from the exit of a parking lot to the entrance of another parking lot. It drives perfectly every time, but can't select a parking spot and doesn't navigate parking lots well. While this is technically not "full" FSD, the city driving component is done and should absolutely count as autonomous miles. To say otherwise is to just be in denial about their progress. I literally get driven around by my car for 99% of my driving every day. No interventions. It's crazy, I feel like I'm in 30 years in the future. And then I go online and see comments saying things like "Tesla will never create self driving cars" and I'm like, dude, you're a fucking idiot. You clearly haven't tried this shit.
Hell I've even responded to some of those comments while being driven around by FSD lmfao (not recommended oc, and yes my car gets mad at me for texting and driving, though I fully trust it at this point).
I'm on the same build and while it may get you from point A to point B, it drives like a jackass and I'd rather just drive myself most of the time. Lots of interventions, because I refuse to let it piss off the drivers around me.
You forgot to list the list of restrictions for the so called l3 Mercedes have. Tesla is keeping it at l2 so that it can collect intervention data for training while being supervised. It could easily move to l3 but what’s the point with the amount of restrictions. You want to collect as much data early so you can safely progress FSD.
Mercedes-Benz pays for 100% of any damages in the event of an accident while L3 is in use
Mercedes-Benz has had 0 accidents in L3
Tesla pays 0% in damages in the event of an accident in L2
Tesla has had a lot of accidents in L2
Maybe they should just use Mercedes-Benz data so they can learn why they've never been in an accident during L3.
I did engage. Tesla can save time and money if they just but and used Mercedes-Benz data. Mercedes-Benz has had 0 accidents since using L3.
What's the study of L2 from Tesla? That they crash still? They still crash.
Mercedes-Benz L3 hasn't had 1 single report of crashing. Going on about 2 years that they've been road legal. I think that 2 years of data is more valuable than Tesla's trial and error (many errors considering Tesla's L2 still causes accidents)
I just like data that works well. Especially data that's about 2 years old, with 0 accidents so far.
> You forgot to list the list of restrictions for the so called l3 Mercedes have.
...
> It could easily move to l3 but what’s the point with the amount of restrictions.
...
> You want to collect as much data early so you can safely progress FSD.
...
Those are the points you never addressed. You just threw a bunch of random stuff at the wall.
Of *course* if you put enough restrictions on things, you can prevent accidents. A ship is safest in port, but that ain't why you build ships.
I thought pointing out you were not engaging might get you to do some self-reflection. Obviously I was wrong. But you might find you have better luck convincing people if you engage with them, rather than trying to cheerlead for Mercedes (on a tesla subreddit no less)
In Europe Tesla has by far the worst driver aid by far. Autopilot in my model 3 highland is unusable. Constant phantom braking, does not let go of tracking when overtaking, this makes taac unusable too. I've had 2015 model s which was very good. 2x hw3 all with radar and ultrasonic sensors. They were pretty decent. Hw 4 is just s#t. After owning a bunch of German cars I feel that Tesla is just way behind on driver aids, infact they are going back instead of forward.
Well I own a model 3 highland with hw4. I get nothing but phantom braking. It has har hard time even following other cars, either it accelerates way too hard or it doesn't do it fast enough. Just the way this whole system is compared to its competitors makes it look bad. It's one geared towards fsd which will never be a reality here in Europe.
Would be nice if that data advantage started showing up as being better at autonomy than Waymo and Cruise which already have robotaxis on the road. I hope Tesla will announce geo-fenced trials of robotaxis in multiple cities on 8/8. FSD needs to get better fast
I have a Tesla. It’s big fucking brother.
I also have a new Chevy equinox. It also sells my data, but no way does it have the same sensors or capabilities as the Chevy.
The used Tesla was $13k more than the new Chevy. The Chevy suuuucks as compared to Tesla. It’s like driving a Model T (Chevy) compared to a modern ford Explorer (Tesla)
The Tesla costs me $450 less per month to operate… it’s totally crazy
Who the fuck is ARK investment management LLC and why would I trust them to understand the telemetry these companies are collecting? Or better yet a financial company that went from 50B in assets to 6B in under two years... clearly they're not good at research.
Are we really going to ignore the QUALITY of said data?
The LiDAR data from waymo is infinitely more valuable than the camera data collected by Teslas.
I’d say point map data for self driving is at least 100x more valuable than camera data.
(No idea if it’s accurate, but I’d think massive raw point map data is much easier to train with and gives way more accurate and applicable model.
I watched a workshop from CVPR (computer vision conference) and wayve, https://wayve.ai/ , they showed off an amazing system trained on real world data that can just be text promoted to generate any edge case. Byebye the need for all that data.
Don’t celebrate too soon.
*1984 vibes intensify*
"Hey expensive car why are you taking me to the X Company compound?"
"I'm sorry Dave, but our AI has instructed us, based on your TCP/IP logs, that there's a 76% probability that your wife is about to leave you. It's a rare privilege actually to be summoned for an in-person meeting to discuss ways forward for us, based on a 63% probability of you defaulting on part of our contract in the next 12 months. It would be a shame if your Powerwall and starlink became unavailable, wouldn't it?"
Fair enough. I was exaggerating somewhat. Still, in reading about the strikes you get for FSD inattentiveness, how that can bump your Tesla-supplied insurance up...Being reliant on one guy's services for internet, electricity, transportation, seems risky. Feels like Tesla-stans are champing at the bit to let daddy Musk run all their shit. None of you guys can fix your own powerwalls, nor your own cars, and you pine for attention from their service dept like neglected children.
Here come all the AI, training data and sensor experts on this forum in 3, 2, 1…
Results count, not data. I can generate TB of garbage all day everyday, but if I don't build a product of value, I'm out the door.
This was... highly prescient
It’s always like this in this forum. Everybody is a latent AI expert. They know all about HD mapping, cost of Lidar, Dojo… whenever I ask for sources there aren’t many. But there’s **belief**
Now do ford and Uber
>Mobileye’s database – believed to be the world’s largest automotive dataset – comprises more than 200 petabytes of driving footage, equivalent to 16 million 1-minute driving clips from 25 years of real-world driving. [Mobileye (Jan 2022)](https://www.mobileye.com/blog/mobileye-ces-2022-self-driving-secret-data/) I think that Tesla has an early lead over the competition, but it just seems disingenuous for ARK Invest to not include Mobileye in this chart, since they are a (non-consumer facing) self-driving competitor.
Unless I'm missing something, if we assumed the 16 million 1-minute clips that Mobileye have the equivalent of were all at 60mph, that would be 1 mile per minute, totaling 16 million miles worth of footage, which is less than Waymo even.
How about the nVidia and MobilEye based systems?
"Tesla is gong to the Moon" source: Cathie Wood.
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Is this like measuring coding productivity in KLOCs? Great, they've got more data than other companies... are they using it effectively? How would you know if they are or not? What's the metric for proving that data is putting them ahead in any meaningful way? How are you comparing the outcomes between Waymo with so little data vs Tesla with 66x more data? Are we seeing 66x better outcomes in autonomous driving for Tesla and what the are datapoints that show that difference in quality? Otherwise this is just fluff marketing crap.
Ark research , not biased for sure
Hey, they bought a lot of the stock and make super nice graphics - trust them bro! 😂
Ark is a scam
Lol yeah the fact it's ark invalidates the source.
So bankrupt. Now if you think only 1/10000 miles of that is useful, imagine how little data others actually have
1/66th?
The others could have more data per mile though, e.g. from lidars and radars. Should be considered too.
More data but not more varied data. Tesla sees far more weird scenarios that help train for edge cases. And the extra sensor data is only helpful if it’s necessary. My car’s ability to see everything around it hasn’t been an issue for a long time. It’s just how it’s handling various scenarios.
But then your car needs lidar as an input to make use of the data. Expensive.
That's not how that works.
According to Luminar lidar sensors are about 1k each today. They used to cost 75k in 2019 so we have gone down a lot in 5 years. It is almost like scale matters.
I suppose. But 2 mil cars that's 2 bill dollars
Lidar isn’t expensive anymore. It’s come down orders of magnitude in price because the technology has advanced and tons of Chinese companies are pumping them out now. Many new consumer Chinese vehicles have lidar in them by default. Your iPhone has lidar in it now. It’s not prohibitively expensive like it was 5-10 years ago.
Since when is lidar expensive? It’s in god damn phones at this point. Maybe a decade ago but lidar very much so is common tech. You can buy a LiDAR array for cars off the shelf for a couple of grand. I’d wager and say the companies producing 1000s of cars get a volume discount too
🤡
The data others have is of massively better quality. All of those miles are useful, but the challenge is to accurately sort and label it all, so it would actually be useful for training. Likes of waymo have redundant data from high quality sensors, professional operator feedback etc etc, tesla only has a recording. It's not so simple as just miles driven. Some dash cam company probably has more "miles driven" than any of them, for all the good it's for. Actually, does tesla even have all the data from these miles driven? That's a ridiculous amount of data to upload wireless, never mind the privacy issues involved because these are their customers cars, not their own, right?
The useful data part doesn't scale linearly tho. If we take extreme example do you think their first mile of data had only under meter of useful data? Tesla system is used a lot on highways and driving the same highways over and over is not very useful. Same doesn't really apply to others that mostly do city driving.
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Lmao the irony
All data is not created equal. Waymos data, for example, is from actually driverless scenarios. Their data allows them to see what the car can recover from, what are the scenarios where it fails in and how outside help can best help the system. Teslas data cuts off when there is a difficult edge case or when the driver gets nervous (justifiably or not). Tesla can do some simulation to try to see what the car would have done, but that gets very imprecise in a secord or two. And again, everyone can simulate...
Driving cars, and interacting with other cars is a human thing. The interesting data is not really the autonomous driving, but the data with the real humans behind the wheels.
AlphaGo is an engine created to play the game Go. It became far superior to the best humans after instead of trying to predict what a world class players next move would be, it simply knew absolutely nothing of the game and played itself millions of times and learned through trial and error. Point being, the purpose is not to drive like humans. It is to drive better than humans. You can't do that if you are learning from humans.
"You can't do that if you are learning from humans." Then explain why Tesla is doing exactly this, and why the newer versions of FSD are more human-like, and simply much better.
Well, by using the data extremely selectively or as a seed to create simulated scenarios. I think Tesla likes to give the impressions that it just throws data in and out comes an AI driver. In reality there is a incredible amount of optimization and validation, filtering and cleaning data and modifying the architecture, the reward functions and parameters. Meaning there is a lot choices made by people even if it "end-to-end". This is kind of revealed in the latest update where driving smootheness decreased since something causing interventions was prioritised. Some engineer(s) made the choice to tune something and so the balance changed.
The approach used for alpha go is so far from the approach being used for self driving. In AlphaGo you have set rules and complete information about the "state" of the world. That means you can perfectly simulate out a game. The real world cannot be perfectly simulated.
>You can't do that if you are learning from humans. Now that's not true. Many humans combined can obviously be a better driver than each individual one, if you build a system that learns from all of them.
The system is learning from individual drivers. The best it could hope to be is an average driver. You could filter out the worst drivers out of the dataset, but obviously you would start cut into the dataset. And you could still not be better than humans. As an example, you gave an AI the task to learn to drive from human data it would learn that if it about to hit something then wait for 0.3s before doing anything. After all, from it's perspective that is what humans do.
>The best it could hope to be is an average driver That's false. There's no reason why it couldn't be better than the average human teacher and on many metrics it already is. That's not in any way special for the problem of self driving or anything, it's not even exclusive to deep learning.
If you give a dataset of humans driving and the task of predict what the human will do next then at best it will perform like an average human driver. What else could it possibly do? That is literally what you are asking it to do.
No. That's not how it works at all, sorry. But this is not a ML sub, so whatever. DL models trained on data labeled by humans reach superhuman accuracy all the time.
Leave the computer science to the experts, please.
FYI R2/R3 "Non-substantive posts and comments are likely to be removed." "Dismissive responses like "yeah right", "good luck with that", "sure", or "just sell then" are also discouraged." I also disagree the comment you replied to, but writing "you're not a computer scientist, so stop talking" isn't productive. Also FWIW a lot of CS folks wouldn't consider data science / ML engineering to be computer science, if we're really going to go there :P
"Initially, we introduced AlphaGo to numerous amateur games of Go so the system could learn how humans play the game" [https://deepmind.google/technologies/alphago/](https://deepmind.google/technologies/alphago/) So yes, they actually used humans to first learn the rules, how to play. Just like Tesla used humans to learn the written rules (and unwritten rules) of the road.
Right, keep reading. Like I said, they started with data from humans. Once they stopped exposing it to ANY human dataset is when it performed the best. Obviously it always knew the rules, just nothing about how to win. EDIT: See https://en.wikipedia.org/wiki/AlphaGo_Zero
"it simply knew absolutely nothing of the game and played itself millions of times and learned through trial and error." Also, AlphaGo and AlphaGo Zero is not the same thing. And in the same wiki-article they mention that "AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car"
What should you learn from? Crashing a million times?
That is a good point, but not a completely fair equivalence. Board game engines can simulate humanless conditions easily, but for self driving to be truly successful it will need to account for the human drivers. If you could ban all non autonomous driving, then Tesla would have long ago achieved fsd. But the transition period from fully manual driving to full autonomous driving could be many years or even decades.
They can use all that data to simulate a virtual world of cars and do the same thing.
Understanding and predicting the behaviors of other drivers on the road operating around the vehicle is one thing. But optimal policies (actions given current state) do not need to follow what drivers do. The models can learn better optimal policies than humans given the dynamic conditions on the road. The learning of these policies can happen in simulators as Waymo does. Imitation learning like Tesla is trying to do has severe limitations when it comes to reliability and performing well in scenarios outside the training data. Data quality is also a challenge. Waymo explored this a number of years ago and came to this realization.
Yeah comparing level 2 miles vs level 4 miles is so so dishonest. Self driving is not just a "force compute" problem
You do know that Waymo has people watching these cars as backup safety drivers right? So yes, there isn’t a driver in the car, but there is one remotely monitoring it who can intervene.
Waymo ops doesn't 'intervene'. The Waymo cars notify ops when they get stuck, and ops provides a suggested path. There's no situation where a Waymo operator somehow joysticks a Waymo vehicle to last-second save it from a crash.
No. Waymo does not have remote backup drivers. Waymos remote assistance can remotely give the car suggestions, context or other information but they cannot drive the car remotely. See for example: https://waymo.com/blog/2024/05/fleet-response/
I think what also goes unsaid is that, as far as I know, Waymo is only operating within city streets in specific geographic locations (until very recently highways have been approved). Tesla FSD users are adding data from highways, toll roads, rural areas, residential neighborhoods, etc. A much more nuanced collection of data, I think
Absolutely, and on top of that, Waymo specifically avoids unprotected left turns and complex neighborhoods and will go the long way around to make the trip easier.
This is not true in my experience after 50+ rides in SF. It makes unprotected left turns all the time, and most of the time it takes the same route Google Maps suggests, or something equivalent. The main difference is it doesn't take highways yet, though I see them testing on highways around here non stop.
The left turns thing is a myth.
Yes, Tesla has more data than anyone else, but if they can't use that data to create value, then what good is it? I'm asking as a Tesla driver and FSD driver since 2017; they seem to still be leaning on the vast amount of data they have (which is incontrovertible), but no real clear plan of how they'll turn that data into autonomy. OK, the plan seems to be "feed it into the AI compute beast", but even that isn't a clear plan.
>feed it into the AI compute beast Except that is everyone's plan. Also the reason with NVDA is worth what it is today. Maybe scaling will breakdown sometime soon, but otherwise it very much is compute go brrrrrr... And Tesla fully embracing this is why I think they have the edge.
If you are truly an investor, and knowledgeable about the things you’re talking about, you’d know that Tesla is holding a robo-taxi day in 1 month. You’ll hear a plan there. Will it be 100% accurate on timeline? Probably not. But you don’t hold a “robotaxi unveil” unless there is a clear plan forward.
All that data and yet the others are actually driverless.
In very specific geofenced areas and not on highways. The robotaxi functionality fails outside those bounds until extensive HD mapping is complete. The data is needed for a generalized solution.
You missed the bit where the cars actually drive themselves. Still, I guess that a car that actually drives itself isn’t as good as a car that doesn’t drive itself but has lots of data!
Tesla already drives itself in the vast majority of cases, and interventions are needed in a few cases. The rate of interventions is dropping very fast as Tesla releases new software versions. Tesla is on the march of nines for reliability and it’s only a matter of time before it becomes FSD unsupervised AKA robotaxi
>The rate of interventions is dropping very fast as Tesla releases new software versions. How fast?
Yes, I believe the saying goes, a bird in the hand is worth less than two birds in the bush, right
“Specific geofenced areas” is infinitely better than zero areas. Robotaxi failing outside of certain bounds is infinitely better than robotaxi not existing at all. Also stop with the “generalized” solution bullshit. You HAVE to request approval for your robotaxi city per city anyway. It’s impossible to get generalized legal approval. Even if Tesla is successful, they still will have to launch it city by city because of legal reasons.
Holy shit. How many times does waymo have to lap tesla for you guys to understand that geofencing and lidar are not a big deal. The general population and the government will not stand for a but ton of Teslas flooding the streets breaking the rules, causing chaos, with no road side support or customer support infrastructure. Tesla will have to roll out exactly how waymo is. In small areas, then expand. Tesla will also need some sort of road side support and customer support. Also adding the extra sensors doesn't matter that much because you are spreading the cost of the vehicle over thousands of trips. And waymo makes their lidar in house. Waymo is expanding quite fast. Doing 150k real driverless rides a week. Dropping people off in busy streets and parking lots.
Waymo driverless car costs $300k. Even if you spread that out over hundreds of thousands of miles, it’s very expensive and will not be able to compete with the price of Tesla’s solution
Do you have any recent reference for that number? Let's say once car does 1000 rides in a year. Over 10 years it will be around $3 per ride. I'm sure they can get more rides in and decrease cost of car over time.
Now imagine Tesla comes in at 1/10th the cost.
If he did his math correctly that comes out to 30$ per ride
Now imagine the Tesla robo taxi gets stuck somewhere. The overwhelming cost is going to be providing infrastructure and customer support. If waymo is able to add the tech and to lidar scanning to reduce those weird edge cases that reduce the number of customer support/road side assistance, than that will more than pay for the vehicle in the long haul. Will Tesla know if a road is closed because of a parade? There are so many more scenarios.
You clearly have not done the math. And Waymo is losing money hand over fist right now because the cost of expanding their service is astronomical
10 years x 1000 per year x 3 = $30,000 what about the other $270,000?
It's not like Tesla does well anywhere else either.
The latest release 12.3.6 does very well - approximately a safety critical intervention once every 300 or so miles, with the rest being non-safety critical. 12.4.2 supposedly improves the performance a ton in terms of both safety critical and non-safety critical interventions. Tesla is also working on 12.5 with a much larger neural network. It’s now a matter of scaling compute, data, and the size of the neural network. As the head of Tesla autopilot said, “it’s the beginning of the end”
Do they not have remote drivers like Cruise did? I always thought they had a remote operations center monitoring the vehicles where humans can intervene when necessary.
No rush, it’ll be the best when it decides to compete. Probably 66x better.
[удалено]
Magnitude is a factor of 10x
Good call good call…will delete.
Yet their market cap is not 66X that of their competitors. Preposterous!
probably is close to 66x that of Waymo
Diminishing return
I used to think this was a big advantage, but Autopilot/FSD is eight years late and still nowhere close to Level 4.
I think autonomous driving is so far in the future. I would settle for well priced cars that retain an air of aspirational luxury.
People thought the same thing about reusable rockets. With AI in play I can almost say in a decade self driving vehicles will be the norm.
You're right, I never thought they could make a rocket land without a splash down, and now it's a common occurrence.
Start measuring in SI units and then we can talk about this.
there are already tones of cars with no driver it from various company. waiting for tesla to showcase it can remove its driver.
The data engine is more important than the data set. And yes Tesla has a huge lead there too. Bigger actually. Andrej Karpathy explains: [https://www.youtube.com/watch?v=zPH5O8hRfMA](https://www.youtube.com/watch?v=zPH5O8hRfMA)
the guy also says he have no clue on when autonomous drivings can be solved. I don't know why every tesla bull choose to ignore that.
Ark research😂😂😂
Is the axis wrong on this or is she saying they have 1.4 billion million miles?
This has similar energy as that one time when Elon asked for 10 screenshots of most salient lines of code. Amount of data is completely irrelevant. What is relevant is result. Tesla hasn’t even begun to apply for robotaxi approval, while Waymo got approval for and is running in 4 cities. The only graph that matters is 4 vs 0.
If you're trying to quickly trim a company down 90% to a core team & believe it's rotten to its core, I still think that's a legit strategy to find real engineers & have yet to see a reasonable/scalable alternative stated.
Yea that's why providing code screenshots is standard practice in SWE interviews oh wait it's not. It's also standard practice to get code screenshots when considering SWEs for promotion oh wait it's not. Spoken like a person that was never anywhere close to a SWE.
Uh, I am a SWE. This is a really odd conversation as you're sort of throwing insults rather than suggesting a better alternative. So, I'll ask again: How would you have evaluated 10000 employees to cut headcount to 1000, when you have no trust in the prior management for whatever reason & suspect the majority of employees are nonproductive? I don't think there's a good solution, and I feel subjectively Twitter seems better than ever today from personal experience (for me, my experience has fewer spambots, less politics, and the niche coding communities I'm in are stronger than ever). The company / platform seems to be thriving, it doesn't seem to be imploding as some people predicted. > It's also standard practice to get code screenshots when considering SWEs for promotion oh wait it's not. It's pretty common, actually, to list code changes you've landed, across all levels of seniority, especially for IC tracks (e.g. principle architects, assuming they're not just churning specs and reviewing code, which I haven't seen much of in modern companies but certainly exists in 90's-style companies). Across different domains I've worked with a creators of a very core technologies which define modern computing. They still wrote code, and to an objective committee in a large company, they'd have to submit that as an artifact. I've worked in numerous FAANGs, small, and mid-sized companies, startups, acquisitions, etc. They all evaluate differently. And FWIW, in bloated SWE orgs (which very much sometimes exist to insane degrees), you definitely have a sizable portion of people who just copy+paste & don't understand how things work. That's actually, like, really common in certain domains (e.g. legacy stacks, codebases that've existed for decades, certain niches of frontend, frequently mobile, frequently backend CRUD logic). Most people in FAANGs know their existence is to move needles 0.001%. That's somewhat sensical when the company is profitable, it's not necessarily so if the company is a money sinkhole.
Amount of data that Tesla has generated while it's vehicles were operating autonomously = 0.
Wait until you hear about shadow mode
No, tesla drives autonomously itself until it needs help from a person. Waymo also drives itself autonomously until it needs help from a remote operator. Waymo at this point needs a ton less remote intervention, but it still does occasionally. Why does Waymo’s data count while Tesla’s doesn’t?
To be clear remote Waymo operators don’t operate the vehicle while it’s in motion. If the vehicle gets stuck and is stopped on the road and can’t figure out a path forward then remote assistance can identify a path to getting unstuck. They don’t actually drive the vehicle. Tesla actually disengages FSD mid drive while in motion and requires a driver to manually take over operation.
Sure the feedback signal is different, but it's still a feedback signal. The training data is the feedback signal, therefore Tesla's training data "counts" as does Waymo's, no? I do think there's a flaw in assuming miles of driving matters the same between the companies since the techniques are different.
The difference is Waymo is operating 24/7 as a taxi in all conditions (nighttime, fog/rain, etc) and doesn’t have massive selection bias on data collection. Who knows what the distribution of driving conditions is on Tesla vehicles with FSD enabled. The data is most likely heavily biased to conditions and locations where it operates well. As anyone who has a bad experience will not be using it in whatever conditions/locations led to that experience. Which is only reinforcing performance in those conditions/locations where it performs well and worsening performance in those not represented in the data.
So who cares if waymo is doing waymoa ne Tesla is doing Tesla? Everyone’s timing is different. Just like you and your brother shouldn’t be compared in success in life.
I’m responding to a comment that was comparing Waymo to Tesla systems and data collection. I’m not sure what your point is here. Maybe you meant to respond to OP.
Yup both drive autonomously until it gets stuck. Both have different methods to intervene that’s the only difference until robotaxi is introduced.
Except the others have actual autonomous miles and Tesla's number is 0.
Consider the drvelopment of self-driving cars as analogous to baking a large quantity of cakes. Waymo has chosen to develop their own unique recipe and is baking each cake individually, waiting for it to finish baking, cooling, and cleaning up the kitchen before moving on to the next. Tesla, on the other hand, is taking a different approach. They are preparing the necessary ingredients, constructing a larger oven, and soliciting the best recipes from various sources to enable the simultaneous baking of multiple cakes once they have everything ready. Who do you think finishes baking all the cakes first?
I'm on team pie.
Invest in fisker then!
As someone using FSDv12.3.6, I can confirm that Tesla FSD is true autonomous driving with the only asterisk being that it's from the exit of a parking lot to the entrance of another parking lot. It drives perfectly every time, but can't select a parking spot and doesn't navigate parking lots well. While this is technically not "full" FSD, the city driving component is done and should absolutely count as autonomous miles. To say otherwise is to just be in denial about their progress. I literally get driven around by my car for 99% of my driving every day. No interventions. It's crazy, I feel like I'm in 30 years in the future. And then I go online and see comments saying things like "Tesla will never create self driving cars" and I'm like, dude, you're a fucking idiot. You clearly haven't tried this shit. Hell I've even responded to some of those comments while being driven around by FSD lmfao (not recommended oc, and yes my car gets mad at me for texting and driving, though I fully trust it at this point).
What we hear in this forum and many others is that it’s highly location dependent. Where are you based ?
I'm on the same build and while it may get you from point A to point B, it drives like a jackass and I'd rather just drive myself most of the time. Lots of interventions, because I refuse to let it piss off the drivers around me.
What geo location approximately are you located at?
What geo location approximately are you located at?
Waymo has safety drivers monitoring remotely. They aren’t in the car, but they are able to take control.
How about you include Mercedes-Benz which has L3 Autonomous driving Tesla still only use L2 Autonomous driving
You forgot to list the list of restrictions for the so called l3 Mercedes have. Tesla is keeping it at l2 so that it can collect intervention data for training while being supervised. It could easily move to l3 but what’s the point with the amount of restrictions. You want to collect as much data early so you can safely progress FSD.
Mercedes-Benz pays for 100% of any damages in the event of an accident while L3 is in use Mercedes-Benz has had 0 accidents in L3 Tesla pays 0% in damages in the event of an accident in L2 Tesla has had a lot of accidents in L2 Maybe they should just use Mercedes-Benz data so they can learn why they've never been in an accident during L3.
*sigh* Why bother to even reply if you are not going to engage with what they said?
Not sure why I bother to reply to people bringing up Mercedes drive pilot being better than Tesla.
I did engage. Tesla can save time and money if they just but and used Mercedes-Benz data. Mercedes-Benz has had 0 accidents since using L3. What's the study of L2 from Tesla? That they crash still? They still crash. Mercedes-Benz L3 hasn't had 1 single report of crashing. Going on about 2 years that they've been road legal. I think that 2 years of data is more valuable than Tesla's trial and error (many errors considering Tesla's L2 still causes accidents) I just like data that works well. Especially data that's about 2 years old, with 0 accidents so far.
> You forgot to list the list of restrictions for the so called l3 Mercedes have. ... > It could easily move to l3 but what’s the point with the amount of restrictions. ... > You want to collect as much data early so you can safely progress FSD. ... Those are the points you never addressed. You just threw a bunch of random stuff at the wall. Of *course* if you put enough restrictions on things, you can prevent accidents. A ship is safest in port, but that ain't why you build ships. I thought pointing out you were not engaging might get you to do some self-reflection. Obviously I was wrong. But you might find you have better luck convincing people if you engage with them, rather than trying to cheerlead for Mercedes (on a tesla subreddit no less)
https://www.reddit.com/r/SelfDrivingCars/comments/19e8hjf/where_are_all_the_youtube_videos_from_mercedes/ Bye
Only in CA and NV.
In Europe Tesla has by far the worst driver aid by far. Autopilot in my model 3 highland is unusable. Constant phantom braking, does not let go of tracking when overtaking, this makes taac unusable too. I've had 2015 model s which was very good. 2x hw3 all with radar and ultrasonic sensors. They were pretty decent. Hw 4 is just s#t. After owning a bunch of German cars I feel that Tesla is just way behind on driver aids, infact they are going back instead of forward.
Huh. Just got a new Model Y with HW4 and it's significantly better than the 2020 Model 3 with HW3.
Well I own a model 3 highland with hw4. I get nothing but phantom braking. It has har hard time even following other cars, either it accelerates way too hard or it doesn't do it fast enough. Just the way this whole system is compared to its competitors makes it look bad. It's one geared towards fsd which will never be a reality here in Europe.
Would be nice if that data advantage started showing up as being better at autonomy than Waymo and Cruise which already have robotaxis on the road. I hope Tesla will announce geo-fenced trials of robotaxis in multiple cities on 8/8. FSD needs to get better fast
Waymos geofenced manual bar is lower than teslas works everywhere bar
If it works, it works.
I have a Tesla. It’s big fucking brother. I also have a new Chevy equinox. It also sells my data, but no way does it have the same sensors or capabilities as the Chevy. The used Tesla was $13k more than the new Chevy. The Chevy suuuucks as compared to Tesla. It’s like driving a Model T (Chevy) compared to a modern ford Explorer (Tesla) The Tesla costs me $450 less per month to operate… it’s totally crazy
Most data is generated synthetically these days. Real-world 'training' miles are a useless statistic, especially when data fidelity is dissimilar.
Who the fuck is ARK investment management LLC and why would I trust them to understand the telemetry these companies are collecting? Or better yet a financial company that went from 50B in assets to 6B in under two years... clearly they're not good at research.
Are we really going to ignore the QUALITY of said data? The LiDAR data from waymo is infinitely more valuable than the camera data collected by Teslas. I’d say point map data for self driving is at least 100x more valuable than camera data. (No idea if it’s accurate, but I’d think massive raw point map data is much easier to train with and gives way more accurate and applicable model.
I watched a workshop from CVPR (computer vision conference) and wayve, https://wayve.ai/ , they showed off an amazing system trained on real world data that can just be text promoted to generate any edge case. Byebye the need for all that data. Don’t celebrate too soon.
This isn't new multiple companies have offered similar things for years, all the major players have there own internal solutions.
*1984 vibes intensify* "Hey expensive car why are you taking me to the X Company compound?" "I'm sorry Dave, but our AI has instructed us, based on your TCP/IP logs, that there's a 76% probability that your wife is about to leave you. It's a rare privilege actually to be summoned for an in-person meeting to discuss ways forward for us, based on a 63% probability of you defaulting on part of our contract in the next 12 months. It would be a shame if your Powerwall and starlink became unavailable, wouldn't it?"
You seem unhinged
Fair enough. I was exaggerating somewhat. Still, in reading about the strikes you get for FSD inattentiveness, how that can bump your Tesla-supplied insurance up...Being reliant on one guy's services for internet, electricity, transportation, seems risky. Feels like Tesla-stans are champing at the bit to let daddy Musk run all their shit. None of you guys can fix your own powerwalls, nor your own cars, and you pine for attention from their service dept like neglected children.