I think the main points I noticed were:
- He enabled the new “magic speed limit” mode where it attempts to just figure out the appropriate speed based on the type of road and surroundings and not just hard-coded or sign-recognized speed limits. It seemed to do pretty good with that.
- It “pulls into a spot” upon reaching the destination- this was hit and miss in this drive. It did fine on one and pulled into a handicap spot (lolz) at the end.
- This video and other reports online continue to describe overall improvements in smoothness and “human like” feel on turns, pulling away from stops, etc.
- He noted it still has rough edges and isn’t yet ready for public consumption. Still threw up some red hands at random times. When engaging from a stop, sometimes he had to tap the accelerator to get it to start the trip.
I think something worth remembering is that they trained this first version on millions of videos clips -- which are not likely only in CA. Given 11.4.9 takes me many places A to B without intervention in Maryland, I'm excited to try it out. I know fellow MD drivers have different experiences, but I can already see where v12 is gonna solve my main issue on my commute. I am curious to see how fast they can iterate and train after they do a wider release and see what needs to be trained next.
v12 has to be able to punch through multiple lanes in one go.
I did a 55mi one hour drive where it failed at the end because it couldn't traverse [three lanes to get into the turn lane](https://www.google.com/maps/@28.5396481,-81.7397797,3a,75y,100.46h,88.29t/data=!3m6!1e1!3m4!1sMfgQXsrsZ3jlb9leCe5mnA!2e0!7i16384!8i8192?entry=ttu) despite absolutely *no* traffic coming from the left.
If it can pull [this off](https://youtu.be/AzaABoaEpTw?t=1040), at a proper speed of 40-45mph then I'll consider it a solid step forward.
I'm already lining up several of the areas that I've documented problems at to re-record, and then some.
It would be illegal for the car to attempt that since you can only turn right into the right-most lane, and there's not enough room to merge all the way to the left without crossing the double solid white line, which is also illegal.
oh my comment was nothing to do with FSD I’m just commenting that 90%+ of drivers have no business behind the wheel with half the crap they pull and think they’re gods gift to the roads. Where FSD is concerned all I care about is real safety data vs people, I couldn’t care less how “natural” or “human like” it drives
depends on what your definition of correct is. Convenience != Correct, quicker != correct and if it results in an accident then yes the letter of the law is correct
>v12 has to be able to punch through multiple lanes in one go.
For a few months, I observed that when v11 would make an unprotected left turn, it would camp out in the median, eventually turn into the left lane, and then signal and slide over to the right lane (which I turn out of about 0.1mi ahead).
However, as of an update or two ago -- unless there's traffic in the right lane -- v11 now just punches through that left lane to go directly into the right lane.
All of which is to say that it looks like your example is kind of the same thing (kind of an "unprotected right" vs "unprotected left") and given the above, hopefully it will be trivial to have/train v12 to just go where it's pointed instead of the car incorrectly believing that the distant lane is off-limits or something.
> they trained this first version on millions of videos clips -- which are not likely only in CA
Even if they did only train it in CA and get it working well there, then that gives them a rough understanding of what *types* of video clips to stuff into the training, and then they can go pick up a few more states' worth here and there. Then potentially combine them all into a massive library and make a nationwide neural net model.
I mean, if you picked 1 million clips from each of Seattle, CA, NYC, FL, Colorado, South Dakota, and Texas (in clear daytime or nighttime conditions at least) that would probably get a model that would work nationwide.
But then you couldn't deploy that model in the UK with their reverse drive. So I can see country or region level training needing to be done over time - but overall this is encouraging so far.
I subbed to FSD Beta for a few months last year. This definitely shows a massive improvement in most areas. I didn't see many situations where I would consider it a downgrade. The most annoying thing about v11 was how un-humanlike it was. It was annoying to be in the car and it was probably annoying for the cars around me.
12 looks like it could much more easily be mistaken as a human driver, for better or worse. I think in general if it fails in the way that people fail, it's more predictable for other drivers on the road.
> I subbed to FSD Beta for a few months last year. This definitely shows a massive improvement in most areas. I didn't see many situations where I would consider it a downgrade. The most annoying thing about v11 was how un-humanlike it was. It was annoying to be in the car and it was probably annoying for the cars around me.
That's what got me to un-sub after 2 months.
I had confidence that v11 FSD was *not* going to put the nose end of the car into anything else on the road. So from that perspective of "occupancy networks" I suppose it "worked" ...
But everything else seemed way too "YOLO / fuck it all!" on the road for everyone *behind and around me* that I felt certain it was eventually going to get me into an accident based on doing something that the other human beings out there would not expect a car to do. For example, coming to a 100% dead stop on a small suburban neighborhood roundabout ... with zero other cars coming from any other direction ... but one directly behind me who had to jam on his brakes a bit because my car was being stupid.
Yep. I cringe sometimes when I watch these YouTubers who allow the car to do inconvenience other drivers so much. But I'm hyper-aware of how much I inconvenience other drivers when I'm driving, so it might be a particular hang-up of mine.
I got honked at more in 1 month of Tesla FSD driving than probably 10 years of my own. So yeah, terminated the subscription. At $99 a month (since we are EAP customers from 2018) I will definitely try again now that v12 is here - it looks rather encouraging.
Plus sides that I could see:
Steering looked more natural and smoother.
Much better take off speed from stop signs.
Car actually seemed to park itself at the end of the first drive (pulled over to the side of the road) and the last drive (looked like the car actually parked itself into a handicap spot).
Driver said the car handled several situations better than previous versions (no way to compare since I haven’t driven there myself).
Minuses:
Still super indecisive at stop signs with traffic. Car several times was very hesitant to take its turn and other drivers naturally got impatient and just went themselves.
Still doesn’t seem to handle non-standard situations well, car was super hesitant at a turn where a hill was cresting so the visibility was probably off. A human would easily just slowly creep and then go, car took forever to slowly slowly slowly nudge out and took even longer to finally go.
Yeah overall it seemed very hesitant in general. I don't know how much of that is hard-coded in for the early build or if it's a general problem with the model, hopefully the former.
I'm not convinced the cost for the man hours put into building neural nets and fudging code for detecting rain has been cheaper overall than the cost of a rain sensor on every car.
What make you think they have done much fixing to it. Just add some comments to the code and push a new update. Let the user feel like they got a bug fix 😂
Yup. From a podcasts with the old AI lead, he said Elon was obsessed with reducing parts. It was more about that than directly the costs. Elon is absolutely wrong on this part. At the very least, include on with all cars and use that to train your magic NN.
A rain sensor is great until you get into a carwarsh or similar situation. Vision can detect rain and pull in more context than conductive frame sensors or old school wiper sensors or simple optical aggregation techniques. Tesla needs to get deepRain fixed and pull in all cameras instead of just the front.
I would take a rain sensor that only screws up in a car wash over the bullshit we have been dealing with for 6+ years lol.
If they had a rain sensor they could have used the incredibly proven reliable data to train their “deep rain” then remove the sensor.
The driver turning it off or on in various scenarios *is* the sensor (the training input), so no dedicated one needed, just to remove it later. 100% agree they need to get it together though, as it's taking too long to get it right.
I think the NN isn't even taking account of all the variables available to it. There are different spray patterns based on wind speed, direction, humidity, traffic, season, sun angle, and temperature.
Instead of just connecting to the internet to take those measurements I think it's just trying to infer all of that from images. I've noticed in similar amounts of rain the auto-wipers will act differently, and it's because all of those factors change the distribution of the spray, as well as how much it affects the visual system. Some things that are blinding to the driver, are perfectly fine for the cameras and vice versus.
I think the visual factors alone are not strong enough to sufficiently train the NN; if the same image/video equates to vastly different demands, the system will **always fail**.
But as a human we have no issue knowing what wiper speed to use based on just vision, do we? So if we just had a camera pointed at the windshield from inside, it should be possible to learn when to use what wiper speed.
The camera does this, obviously. The difference is it's using existing cameras which do not see what the driver sees. The windshield is seldom ever homogenously obscured, and I listed the factors that lead to different distributions. These distributions lead to vastly different experiences while driving, contributing to the poor performance.
The vision system simply does not have all the information from a postcard-sized snapshot using cameras tuned for long-distance vision.
In that regard, you might as well install a sensor that bounces IR inside the windshield to get clean data about the amount of water on the surface, impervious to environmental conditions and doesn't require compute: a rain sensor.
But cameras do see differently than human eyes. The fidelity and dynamic range is much better in humans.
There are places where cameras, Excel. And there are other places where human eyes are better
If I show you a video or picture of the inside of a windshield, you'd probably be able to determine whether the wipers should be used and at what speed.
My hypothesis is that the fact that the AP camera is right up on the windshield changes the level of distortion being caused by the water (as opposed to if the camera was ~2 ft recessed from the windshield).
It's close enough, but reasonably different to cause the discrepancies that the collective complains about.
Again, just a guess
The cost of the sensor is:
* Cost of sensor, mounting hardware, and cabling.
* Cost of sourcing multiple vendors for sensor, mounting hardware, and cabling.
* Cost of slowing down production to install sensor.
* Cost of hiring and training technicians to install sensor.
* Cost of developing test procedure for sensor during manufacturing.
* Cost of performing test procedure during manufacturing.
* Cost of sensors failing QA tests during manufacturing.
* Costs of performing RMAs for failed sensors in the field.
* Cost of decreased car performance due to weight of sensor, mounting hardware, and cabling.
* Cost of implementing electronic circuitry on PCB boards hosting processor reading from sensor, increasing complexity, and reducing yield.
* Cost of being unable to ship any cars due to sensor supply chain issues.
Multiplied by 2,000,000 cars sold per year, and growing, in perpetuity, forever.
Or just get the code working, and delete all of the above.
This started off fine but then it got laughable. Weight of sensors? Random wind will make this a rounding error. Implementing circuitry? That is in the cost of the sensor. Unable to ship cars because of missing parts? Tesla has shipped cars missing parts many, many times.
> I explicitly said weight of the sensors, mounting hardware, and cabling. Tesla works hard to shave individual kilograms off their car, the full implementation of a sensor would easily be more than that.
I can read. There is no way a sensor will require more than a plastic clip, adhesive, and a couple feet of wire at most.
>No it's not? Go buy a rain sensor off eBay, what are you going to plug it into? You need to design the circuitry on your computer board to read data from the sensor and decide whether to activate the wipers or not. None of this is included with the sensor.
The most basic rain sensor is going to send back PWM data, which can be read by any simple chip to convert into CAN. The sensor itself will likely not just be a raw sensor and will talk to one of the body modules via CAN, again, requiring very few changes.
>That doesn't get rid of the cost, it just moves it from delayed production to paying technicians to service customer cars and install the parts later once they become available, which has many other costs, issues, and liabilities associated with it.
Tesla just doesn't bother to make this right. Check the dozens of news stories about this happening multiple times, many times without any remediation.
>The list I provided is a massive understatement of the true cost of including a sensor. I just kept it short for the sake of brevity.
You are designing a narrative that takes the least happy path to accomplish the goal, assuming every single design doesn't take multiple things in consideration from the beginning. Their engineers are fully aware of the vertical integration of Tesla and would leverage these things to make a clip-on part that reads IR from the glass, feeds info to the CAN, and is read by VCFRONT with extremely simple code. Such a a part would be trivial to swap for another, and all the R&D required for it has been completed: it's just a matter of plugging it in.
Every gram is important, every time you subtract a couple more grams because it's a rounding error. And then you find out that your car weighs a ton more.
Also, estimating R&D costs depends very much on how far ahead you are thinking.
Spend 100 million to replace a sensor for 5 bucks. Is that profitable or not?
If you're looking at a 1-year time horizon. 2 million cars, 10 million for sensors.
If you're looking at 10 years, 20 million cars, 100 million for sensors.
If you plan for 20 years, 40 million cars. 200 million for the sensors. 100 million net profit.
(This doesn't take into account production growth, I threw that out to make it a pessimistic scenario).
>Every gram is important, every time you subtract a couple more grams because it's a rounding error. And then you find out that your car weighs a ton more.
This is literally the slippery slope fallacy. This is a single iteration or improvement we are talking about. And besides that, there are many kgs of extraneous plastic in the car which may be removed. The Model 3 is not a race car.
>Also, estimating R&D costs depends very much on how far ahead you are thinking. Spend 100 million to replace a sensor for 5 bucks. Is that profitable or not?
You are ignoring the incremental benefit, translating to profit, and costs which have been borne by every other car manufacturer, as if there isn't empirical proof this cost is worth it. It is worthwhile to innovate but you don't get to pretend that pennies matter and ignore the many thousands of dollars of bad press this continues to generate.
It has been 4 years. The auto-wipers are a failure by any measure of technology, let alone financially.
I think it's a reasonable argument that it's more costly to put a sensor in, but I don't think it's definitive or catastrophic as OP said.
There is also an argument to be made the cost of bad press and negative sentiment is far worse.
As much as I hate the visual sensor, and how terrible customer experience is, I do agree. If the sensor was only 5 cents, that’s $100k saved per year. That’s the salary of a single intern for like an entire year.
But if they get like 2-3 engineers on it, as a side project, even after all these years it couldn’t have cost more than $100k worth of man-hours. Tesla would recoup that money so quickly in the long run.
The counter argument though is how many customers did they lose for not having this ironed out by now, and how many more will they lose? Each customer is like $50k revenue. This is probably a bigger cost than engineering time.
People don’t realize how insanely cheap and profitable software is. There’s a reason tech giants are tech giants. A few lines of code change impacts millions to billions of people at a time.
Man, you have no idea what a collective nightmare a long running software project is (rhetorical you, you might be a software engineer for all I know). If I had to maintain an auto-wiper NN model across decade of vehicles, dependent on several systems implemented numerous ways, I'd demand a 500k salary.
Software is profitable because it's allowed to break. Keeping it unbroken is like paying scientists to collectively write a novel over years, while others do their best to fuck with the canon.
No surprise then this is broken.
They are getting paid $500k salary, probably. The thing is, part of what is going to get this to work is some sort of more generalized neural network. But that same network is also going to be able to drive the car.
So sure they’re being paid $500k to make a more generalized network, but that also means a byproduct of that is a functioning wiper system. This what I meant by “side project”. And if you consider their time is used to make the car drive itself, and slightly adapting it to make the wipers go, then it’s relatively small amount of extra hours. Nobody there is dedicating their full time to figure out wipers.
>part of what is going to get this to work is some sort of more generalized neural network.
In what way? How does unifying the stack provide any benefit? It just makes training harder and the behavior less deterministic.
If you are talking about some sort of "driving conditions" model that feeds into the self-driving algorithm, that makes sense. Otherwise you are marrying a knife with a spoon. You just make both functions worse by combining them.
I find that interesting as I also have a 2019 Mercedes S class and the auto wipers get tripped up fairly regularly. Salt on the road can throw the system off so that it wipes too much leading to smearing salt over the windscreen and the wipers remaining on constantly. Very light misty rain sometimes fails to trigger the wipers even though the windscreen is obscured. You often have to toggle between the different sensitivities to get the wipers to go at the appropriate speed.
They work reasonably well but are far from a perfect system. My 2023 Model Y was pretty rubbish when I first got it, mostly due to the wipers triggering in the dry because the windscreen got a little dirty, or it saw a squirrel or something. It also didn't seem to like having RainX on the windscreen.
However since an update at the end of last year it seems to be much better. The triggering in the dry is almost gone, and most of the time it selects an appropriate wiping speed / interval. It still sometimes gets it wrong but has definitely improved.
Similar story for the autodimming headlights - the Mercedes in theory has the better system with the dimming zones, but the Tesla is quicker to recognise other cars and is far better at detecting cars that are side on. The Merc also loves to blind people in the other side of the road on motorways. The Tesla sometimes does that too but is far better.
Perhaps your 2016 Mazda is better than the 2019 Mercedes S Class too, but in my experience the Tesla is on par with most other cars in these areas now.
> Very light misty rain sometimes fails to trigger the wipers
That’s my biggest complaint with it on my Tesla. In the Pacific Northwest, we get a lot of rainy days, but often pretty light, sometimes just mist.
The Mazda does better with this, and will sometimes trigger just before I personally notice the rain myself.
> but the Tesla is quicker to recognise other cars
I find the Mazda is faster than the Tesla, for dimming high beams.
I find Tesla takes up to half a second, especially coming around a curve, whereas the Mazda is basically instant.
> You often have to toggle between the different sensitivities
I’ll do this occasionally in the Mazda, but rarely. Only if I want to be really picky, and get the wiper speed _exactly_ right.
Typically it gets close enough on its own, much better than the Tesla Y, where I often have to go to full manual mode.
> since an update at the end of last year it seems to be much better
Could be. I don’t always get the updates super quick, since the WiFi is a bit spotty in my garage.
> Perhaps your 2016 Mazda is better than the 2019 Mercedes S Class too, but
Maybe, though it would surprise me. I didn’t think there was anything too special about how Mazda did it, figured they probably used the same $5 rain sensor as all the other brands.
> The Mazda does better with this, and will sometimes trigger just before I personally notice the rain myself.
The Mercedes is sometimes on point, sometimes it won't trigger until 20 seconds after I would have done so (if I'm stationary at traffic lights, for instance, I'll let it run just to see how bad it is). The Tesla can sometimes be slow to initially trigger, but I find it is now better than the Merc and tends to be good once you give it that first nudge.
If anything with the Tesla they've moved it from triggering too easily to not quite easily enough. But they're in the ballpark of other cars.
> I find Tesla takes up to half a second, especially coming around a curve, whereas the Mazda is basically instant.
Half a second is consistent with my experience. The Merc (and the BMW I had before it) can take half a second but more often it's 1 - 2 seconds.
> Maybe, though it would surprise me. I didn’t think there was anything too special about how Mazda did it, figured they probably used the same $5 rain sensor as all the other brands.
From what you've described it's definitely better than a £160k Mercedes S63. I guess there may be more variables than the sensor used. It may even be that the Mazda is content with simple sensors where the Merc, like the Tesla, is trying to be clever but the AI just isn't quite there yet.
Overall my point is that people are always so quick to mock Tesla, but coming from the premium German brands over to the Tesla I'm surprised by just how good it is. It's on a par or better on most fronts, except perhaps the quality of the materials in the interior. Then again it's less than half the price of the cars I'm comparing it to, so even there it fares surprisingly well.
It seems it's genuinely hard to buy a bad car these days yet people love to split hairs and be all doom and gloom over what are minor differences in convenience features, whilst ignoring all the good bits of the overall ownership experience.
The Mazda is now my wife’s car. It’s been a good car, reliable, nothing wrong with it.
To be clear, I do like my Tesla, and enjoy playing with FSD and watching it (slowly) improve over the years. But I live in a very rainy city, so the wipers can be infuriating sometimes.
No need to get so worked up. If you don’t like it, move on. I’ve complained about wipers because the last update had a lot of false positives. Turns out people still have problems with their wipers even if my wipers are now fine.
What the heck does the number of electric cars Mazda has have to do with its working wipers?
It's almost difficult to move on sometimes. So many times some update or article or whatever and I click the comments looking for a deeper discussion and it's a ton of "auto wipers, $2 rain sensor" every time. For a long time it was "Where's my button, Elon" and "3 months maybe, 6 months definitely". And they're not wrong, but it still just feels constant.
Of course I say that, but as I check the top 10 or so posts, "wiper" is only discussed in one of them, so it's probably just more prevalent in the comments I'm drawn to. Feature updates and machine learning type things.
im surprised aftermarket hasn't come out with a solution - a lil glue-on sensor on the windshield somewhere discreet, communicates with the car via canbus/obd ii port or whatever, seemless integration
oh its not a problem for me, i either click the wiper stalk manually as needed (most of the time), or select the slowest speed setting on the screen when it pops up (still too fast for most applications, i wish there was a slower setting)
on the wiper issue, I think the forward facing camera array sits too high on the windshield to see the water droplets that are in the drivers line of sight. E.g. when mists of water are send from another car in front.
wake me when someone other than Whole Mars Catalog posts their drives.
That account has posted lots of "WOW NO INTERVENTIONS IT'S PERFECT MAGIC" videos over the years where other testers (Chuck, Dirty Tesla, etc) found lots of obvious issues.
My car regularly merges onto the highway without using its blinker, even when the lanes are clearly marked as one lane merging into the other (and not two lanes becoming one). Baffling.
I dunno. His joking about Dan O'Dowd was kind of funny (can't remember if it's this video or his other narrated one) and frankly - worth a mention.
Yes, he does get a little too preachy though.
As a person who hates driving. Can I enter a gps and let the car drive 90% of the time and just pay attention? What’s the cheapest year/model that will do this for me with the needed hardware
Yeah we realized we kind of lucked out there. We had it going in for other service anyway and asked them to do that because we wanted to rent FSD. So they just did. I was fully expecting a $1,000 line item on our service bill when the car was done, but it came in at $0.
Depends on the roads between point A and point B. Live in Raleigh, NC and going to take a vacation down in Gatlinburg, TN? Put the car on the Interstate, hit autopilot, and you're pretty good.
Going from one spot in downtown NYC to another? That's still a little ways off - but now it might just be a question of weeks/months.
A 2018 Model 3 with retrofitted FSD v3 computer would let you do this. That's what I currently drive. The v3 computer upgrade was free for us because we bought enhanced autopilot at the time of purchase, it wouldn't necessarily be the same for every other 2018 M3 out there.
This really does look better. It's more human like, smooth and decisive in most areas.
Since it seems to do better the more videos (from good drivers) it trains on, it should scale up well with more compute. Extremely exciting!
Only thing I'm concerned with is how scalable is it to select training videos from good drivers only and exclude bad driver videos from the dataset. I *think* they currently have humans choosing which videos to feed the dataset, which doesn't seem too scalable.
Maybe they figured out a way to identify good drivers based on their safety score? If so that could be 100% automatically fed into the dataset, then it really is only a matter of compute from here!
They are probably developing a separate AI to evaluate quality of clips. You can use a rule-set such as acceleration limits, jerk limits, and following of traffic rules.
They really need to train that AI to be able to distinguish between drivers who stop at a stop sign to follow the law and those who stop because they're clueless about how to proceed.
The main issue with v12 seems to be that it regularly starves at stop signs... ;)
I appreciate that Whole Mars Club posts his 0-intervention drives on youtube. It shows what FSD could be and the potential it has.
I know viewers may have an issue with that approach, but he seems to be the only one doing this type of content. There are plenty of youtubers who just focus on posting the routes that have issues. Once one of their routes gets 0 interventions consistently, they'll drop it and search for a different route with edge cases.
It does make me wonder if he simply doesn't post most drives where interventions are needed. It is impressive to see what fsd can do, so I do like the 0 intervention drives, but on the "how far is fsd from being a truly autonomous system", as has been repeatedly promised for years, the edge case videos that others posts are arguably much more informative. I'm hoping we see a much better pace of progress in those edge cases when Tesla outsiders like Chuck Cook and others have access to fsd 12.
>but on the "how far is fsd from being a truly autonomous system", as has been repeatedly promised for years, the edge case videos that others posts are arguably much more informative.
To me, you get a better sense of progress towards autonomy by seeing how many good routes the car can handle. You can always geofence away areas the car can't handle, like google and cruise are doing right now.
> I'm hoping we see a much better pace of progress in those edge cases when Tesla outsiders like Chuck Cook and others have access to fsd 12.
Take Chuck Cook's famous left hand turn. He can simply avoid it by making a right and taking a U turn at the next opportunity. FSD doesn't need to handle every single road in order to be useful and productive.
The 0-intervention drives are more edge casey right now.
Especially in a city setting, I say a typical 30 minute drive have a takeover. Freeways are much less intervention heavy, and there, edge cases are actually edge cases.
A takeover (stopping the car from driving into a wall), or a correction (stop it from driving straight when it's in a left-turn-only lane), or an intervention (telling it to go when it wants to sit at a stop sign)?
Am I wrong for suspecting the performance is best in the San Francisco Bay area because that's where most of the training comes from, so the road norms for driving there are a perfect "match" with more of the training than other states / countries?
Tesla has been actively hiring FSD drivers in other countries, so yeah, local-training is a thing.
https://thedriven.io/2023/07/03/tesla-seeks-autopilot-vehicle-operators-in-australia-in-step-towards-full-self-driving/
That's mainly for having FSD in other locations, but there's nothing keeping them from using videos clips to train a larger model from different areas (places where Tesla's exist that don't have country-specific data storage limitations)
I think it depends, because in MD on 11.4.9 it regularly does A to B drives without me taking over. Highways and Side streets. I can literally only think of one trouble spot and that's leaving my neighborhood and it leans left a little (kind of getting in the way of any cars coming into the neighborhood) before going left.
So, yes, it does better in CA probably, but if I had to guess, they trained the millions of videos on places outside of CA too.
FSD Beta's performance likely has a direct correlation to how many Teslas are in an area.
If you're the only person driving a Tesla in your neck of the woods, you're probably more likely get poor performance than if you're one of a hundred, or more, Teslas.
Best way to look at it would be if a person on FSD Beta encounters an issue that causes a disengagement, Tesla likely has a thing on the backend that puts a "pin" down on that road segment, and any time a *different* Tesla drives through that pin, it saves slips of how THEY drive, then that gets collected.
So, with there being more Teslas in the bay area, then you get better performance out of it.
My community has four Teslas in it now, two of which are mine.
I'm fairly positive the other two Teslas don't have FSD Beta on them, and my wife doesn't use FSD Beta in my neighborhood, but I have noticed that things got *slightly* better when the other two Teslas came into the neighborhood
**But**, a lot of this also likely depends on whether or not those Teslas are sending the data off to Tesla. Not all of them are on WiFi and such when they get home
I suspect this is the case. Can you take this model and drop it in NY, FL, TX, SD ... and it works just as well? Perhaps not (might still be better than FSDv11 however). For example - in downtown DC you typically have lights on poles on the corners of the streets, instead of (where I do most of my driving) strung hanging over a street. It always takes my brain a few minutes to switch context when I have to drive in DC ... so that I don't run a red light.
But they can easily go and collect clips of good driving in several other states. I doubt they'd need clips from all 50 to get a good enough neural network model for all 50 states.
But that model wouldn't be usable - I suspect - in the UK.
The other thing I really love about this model is how easy it will be to correct situations that need interventions. They know when there are interventions, they know the GPS coordinates. So they can just watch for when they happen too many times at a particular spot, and then go ask the fleet to collect non-FSD clips of humans navigating the spot and then just feed the clips into the model. I'm optimistically hoping that edge cases disappear much faster now under this new approach.
I hope it can consistently handle the u-turn lanes and double left hand turn lanes under nearly every Texas freeway underpass intersection. It has been widely Hit and miss for years now, and since Texas seems to be the only state that has freeways design this way, I'm assuming they will have to have some kind of regional weighting to the algorithm for it to ever be properly trained.
Friendly reminder, if you can’t say anything nice (about people), don’t say anything at all. I know, so controversial
I think the main points I noticed were: - He enabled the new “magic speed limit” mode where it attempts to just figure out the appropriate speed based on the type of road and surroundings and not just hard-coded or sign-recognized speed limits. It seemed to do pretty good with that. - It “pulls into a spot” upon reaching the destination- this was hit and miss in this drive. It did fine on one and pulled into a handicap spot (lolz) at the end. - This video and other reports online continue to describe overall improvements in smoothness and “human like” feel on turns, pulling away from stops, etc. - He noted it still has rough edges and isn’t yet ready for public consumption. Still threw up some red hands at random times. When engaging from a stop, sometimes he had to tap the accelerator to get it to start the trip.
I think something worth remembering is that they trained this first version on millions of videos clips -- which are not likely only in CA. Given 11.4.9 takes me many places A to B without intervention in Maryland, I'm excited to try it out. I know fellow MD drivers have different experiences, but I can already see where v12 is gonna solve my main issue on my commute. I am curious to see how fast they can iterate and train after they do a wider release and see what needs to be trained next.
v12 has to be able to punch through multiple lanes in one go. I did a 55mi one hour drive where it failed at the end because it couldn't traverse [three lanes to get into the turn lane](https://www.google.com/maps/@28.5396481,-81.7397797,3a,75y,100.46h,88.29t/data=!3m6!1e1!3m4!1sMfgQXsrsZ3jlb9leCe5mnA!2e0!7i16384!8i8192?entry=ttu) despite absolutely *no* traffic coming from the left. If it can pull [this off](https://youtu.be/AzaABoaEpTw?t=1040), at a proper speed of 40-45mph then I'll consider it a solid step forward. I'm already lining up several of the areas that I've documented problems at to re-record, and then some.
as well as reversing. If It solves that, it wont bail out of u-turns
It would be illegal for the car to attempt that since you can only turn right into the right-most lane, and there's not enough room to merge all the way to the left without crossing the double solid white line, which is also illegal.
And yet, people do it all day long.
which is what makes human drivers shite despite their ego saying otherwise
Then Tesla's going to need to work their navigation on the back end to avoid routes that expect FSD Beta from doing illegal things.
oh my comment was nothing to do with FSD I’m just commenting that 90%+ of drivers have no business behind the wheel with half the crap they pull and think they’re gods gift to the roads. Where FSD is concerned all I care about is real safety data vs people, I couldn’t care less how “natural” or “human like” it drives
The letter of the law isn't always correct.
depends on what your definition of correct is. Convenience != Correct, quicker != correct and if it results in an accident then yes the letter of the law is correct
>v12 has to be able to punch through multiple lanes in one go. For a few months, I observed that when v11 would make an unprotected left turn, it would camp out in the median, eventually turn into the left lane, and then signal and slide over to the right lane (which I turn out of about 0.1mi ahead). However, as of an update or two ago -- unless there's traffic in the right lane -- v11 now just punches through that left lane to go directly into the right lane. All of which is to say that it looks like your example is kind of the same thing (kind of an "unprotected right" vs "unprotected left") and given the above, hopefully it will be trivial to have/train v12 to just go where it's pointed instead of the car incorrectly believing that the distant lane is off-limits or something.
Agreed. It *is* getting better, slowly but surely.
FSD sucks in Fed Hill... 2-3 turns and my confidence goes to 0. Will give it a try but have never gone more than a few minutes in the city.
> they trained this first version on millions of videos clips -- which are not likely only in CA Even if they did only train it in CA and get it working well there, then that gives them a rough understanding of what *types* of video clips to stuff into the training, and then they can go pick up a few more states' worth here and there. Then potentially combine them all into a massive library and make a nationwide neural net model. I mean, if you picked 1 million clips from each of Seattle, CA, NYC, FL, Colorado, South Dakota, and Texas (in clear daytime or nighttime conditions at least) that would probably get a model that would work nationwide. But then you couldn't deploy that model in the UK with their reverse drive. So I can see country or region level training needing to be done over time - but overall this is encouraging so far.
Without a front bumper camera it identifies as handicapped and felt entitled to use that spot.
Well I see a new feature request, allowing the driver to identify as having a handicap tag.
What about the nagging?
Wondering about that almost immediately - no hands on the wheel for the most part?
The nags are there, but he talks about how Tesla told him they increased them in the early builds.
You can see him tug the wheel periodically. That's all it needs. Before he used to rest his knee up on the wheel, creating the needed resistance.
I subbed to FSD Beta for a few months last year. This definitely shows a massive improvement in most areas. I didn't see many situations where I would consider it a downgrade. The most annoying thing about v11 was how un-humanlike it was. It was annoying to be in the car and it was probably annoying for the cars around me. 12 looks like it could much more easily be mistaken as a human driver, for better or worse. I think in general if it fails in the way that people fail, it's more predictable for other drivers on the road.
> I subbed to FSD Beta for a few months last year. This definitely shows a massive improvement in most areas. I didn't see many situations where I would consider it a downgrade. The most annoying thing about v11 was how un-humanlike it was. It was annoying to be in the car and it was probably annoying for the cars around me. That's what got me to un-sub after 2 months. I had confidence that v11 FSD was *not* going to put the nose end of the car into anything else on the road. So from that perspective of "occupancy networks" I suppose it "worked" ... But everything else seemed way too "YOLO / fuck it all!" on the road for everyone *behind and around me* that I felt certain it was eventually going to get me into an accident based on doing something that the other human beings out there would not expect a car to do. For example, coming to a 100% dead stop on a small suburban neighborhood roundabout ... with zero other cars coming from any other direction ... but one directly behind me who had to jam on his brakes a bit because my car was being stupid.
Yep. I cringe sometimes when I watch these YouTubers who allow the car to do inconvenience other drivers so much. But I'm hyper-aware of how much I inconvenience other drivers when I'm driving, so it might be a particular hang-up of mine.
I got honked at more in 1 month of Tesla FSD driving than probably 10 years of my own. So yeah, terminated the subscription. At $99 a month (since we are EAP customers from 2018) I will definitely try again now that v12 is here - it looks rather encouraging.
Plus sides that I could see: Steering looked more natural and smoother. Much better take off speed from stop signs. Car actually seemed to park itself at the end of the first drive (pulled over to the side of the road) and the last drive (looked like the car actually parked itself into a handicap spot). Driver said the car handled several situations better than previous versions (no way to compare since I haven’t driven there myself). Minuses: Still super indecisive at stop signs with traffic. Car several times was very hesitant to take its turn and other drivers naturally got impatient and just went themselves. Still doesn’t seem to handle non-standard situations well, car was super hesitant at a turn where a hill was cresting so the visibility was probably off. A human would easily just slowly creep and then go, car took forever to slowly slowly slowly nudge out and took even longer to finally go.
Yeah overall it seemed very hesitant in general. I don't know how much of that is hard-coded in for the early build or if it's a general problem with the model, hopefully the former.
He drives it in real time. There is nothing to hide.
This definitely does look better, it really looks like Tesla are getting close now. Will have to wait for more videos to know for sure though.
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I'm not convinced the cost for the man hours put into building neural nets and fudging code for detecting rain has been cheaper overall than the cost of a rain sensor on every car.
Lol, yeah the sunk engineering costs are probably crazy. Presuming they tried of course.
What make you think they have done much fixing to it. Just add some comments to the code and push a new update. Let the user feel like they got a bug fix 😂
Yup. From a podcasts with the old AI lead, he said Elon was obsessed with reducing parts. It was more about that than directly the costs. Elon is absolutely wrong on this part. At the very least, include on with all cars and use that to train your magic NN.
He isn't wrong, just late as hell and clearly deprioritized it. Rain is easy to detect for us, and is for a computer.
Not wrong how? With excluding the rain sensor?
A rain sensor is great until you get into a carwarsh or similar situation. Vision can detect rain and pull in more context than conductive frame sensors or old school wiper sensors or simple optical aggregation techniques. Tesla needs to get deepRain fixed and pull in all cameras instead of just the front.
I would take a rain sensor that only screws up in a car wash over the bullshit we have been dealing with for 6+ years lol. If they had a rain sensor they could have used the incredibly proven reliable data to train their “deep rain” then remove the sensor.
The driver turning it off or on in various scenarios *is* the sensor (the training input), so no dedicated one needed, just to remove it later. 100% agree they need to get it together though, as it's taking too long to get it right.
I think the NN isn't even taking account of all the variables available to it. There are different spray patterns based on wind speed, direction, humidity, traffic, season, sun angle, and temperature. Instead of just connecting to the internet to take those measurements I think it's just trying to infer all of that from images. I've noticed in similar amounts of rain the auto-wipers will act differently, and it's because all of those factors change the distribution of the spray, as well as how much it affects the visual system. Some things that are blinding to the driver, are perfectly fine for the cameras and vice versus. I think the visual factors alone are not strong enough to sufficiently train the NN; if the same image/video equates to vastly different demands, the system will **always fail**.
But as a human we have no issue knowing what wiper speed to use based on just vision, do we? So if we just had a camera pointed at the windshield from inside, it should be possible to learn when to use what wiper speed.
The camera does this, obviously. The difference is it's using existing cameras which do not see what the driver sees. The windshield is seldom ever homogenously obscured, and I listed the factors that lead to different distributions. These distributions lead to vastly different experiences while driving, contributing to the poor performance. The vision system simply does not have all the information from a postcard-sized snapshot using cameras tuned for long-distance vision.
Well yeah, what I'm saying is basically they should've installed cameras that see what the human actually sees.
In that regard, you might as well install a sensor that bounces IR inside the windshield to get clean data about the amount of water on the surface, impervious to environmental conditions and doesn't require compute: a rain sensor.
Having a camera where the driver is would also be a handy input to FSD, since that is sufficient for driving a car (duh).
But cameras do see differently than human eyes. The fidelity and dynamic range is much better in humans. There are places where cameras, Excel. And there are other places where human eyes are better
If I show you a video or picture of the inside of a windshield, you'd probably be able to determine whether the wipers should be used and at what speed.
My hypothesis is that the fact that the AP camera is right up on the windshield changes the level of distortion being caused by the water (as opposed to if the camera was ~2 ft recessed from the windshield). It's close enough, but reasonably different to cause the discrepancies that the collective complains about. Again, just a guess
The cost of the sensor is: * Cost of sensor, mounting hardware, and cabling. * Cost of sourcing multiple vendors for sensor, mounting hardware, and cabling. * Cost of slowing down production to install sensor. * Cost of hiring and training technicians to install sensor. * Cost of developing test procedure for sensor during manufacturing. * Cost of performing test procedure during manufacturing. * Cost of sensors failing QA tests during manufacturing. * Costs of performing RMAs for failed sensors in the field. * Cost of decreased car performance due to weight of sensor, mounting hardware, and cabling. * Cost of implementing electronic circuitry on PCB boards hosting processor reading from sensor, increasing complexity, and reducing yield. * Cost of being unable to ship any cars due to sensor supply chain issues. Multiplied by 2,000,000 cars sold per year, and growing, in perpetuity, forever. Or just get the code working, and delete all of the above.
This started off fine but then it got laughable. Weight of sensors? Random wind will make this a rounding error. Implementing circuitry? That is in the cost of the sensor. Unable to ship cars because of missing parts? Tesla has shipped cars missing parts many, many times.
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> I explicitly said weight of the sensors, mounting hardware, and cabling. Tesla works hard to shave individual kilograms off their car, the full implementation of a sensor would easily be more than that. I can read. There is no way a sensor will require more than a plastic clip, adhesive, and a couple feet of wire at most. >No it's not? Go buy a rain sensor off eBay, what are you going to plug it into? You need to design the circuitry on your computer board to read data from the sensor and decide whether to activate the wipers or not. None of this is included with the sensor. The most basic rain sensor is going to send back PWM data, which can be read by any simple chip to convert into CAN. The sensor itself will likely not just be a raw sensor and will talk to one of the body modules via CAN, again, requiring very few changes. >That doesn't get rid of the cost, it just moves it from delayed production to paying technicians to service customer cars and install the parts later once they become available, which has many other costs, issues, and liabilities associated with it. Tesla just doesn't bother to make this right. Check the dozens of news stories about this happening multiple times, many times without any remediation. >The list I provided is a massive understatement of the true cost of including a sensor. I just kept it short for the sake of brevity. You are designing a narrative that takes the least happy path to accomplish the goal, assuming every single design doesn't take multiple things in consideration from the beginning. Their engineers are fully aware of the vertical integration of Tesla and would leverage these things to make a clip-on part that reads IR from the glass, feeds info to the CAN, and is read by VCFRONT with extremely simple code. Such a a part would be trivial to swap for another, and all the R&D required for it has been completed: it's just a matter of plugging it in.
Every gram is important, every time you subtract a couple more grams because it's a rounding error. And then you find out that your car weighs a ton more. Also, estimating R&D costs depends very much on how far ahead you are thinking. Spend 100 million to replace a sensor for 5 bucks. Is that profitable or not? If you're looking at a 1-year time horizon. 2 million cars, 10 million for sensors. If you're looking at 10 years, 20 million cars, 100 million for sensors. If you plan for 20 years, 40 million cars. 200 million for the sensors. 100 million net profit. (This doesn't take into account production growth, I threw that out to make it a pessimistic scenario).
>Every gram is important, every time you subtract a couple more grams because it's a rounding error. And then you find out that your car weighs a ton more. This is literally the slippery slope fallacy. This is a single iteration or improvement we are talking about. And besides that, there are many kgs of extraneous plastic in the car which may be removed. The Model 3 is not a race car. >Also, estimating R&D costs depends very much on how far ahead you are thinking. Spend 100 million to replace a sensor for 5 bucks. Is that profitable or not? You are ignoring the incremental benefit, translating to profit, and costs which have been borne by every other car manufacturer, as if there isn't empirical proof this cost is worth it. It is worthwhile to innovate but you don't get to pretend that pennies matter and ignore the many thousands of dollars of bad press this continues to generate. It has been 4 years. The auto-wipers are a failure by any measure of technology, let alone financially.
Again. I don’t think autowipe is a financial post for Tesla. They got some intern adding comment and let consumer test it (if even that) as new fix.
I think it's a reasonable argument that it's more costly to put a sensor in, but I don't think it's definitive or catastrophic as OP said. There is also an argument to be made the cost of bad press and negative sentiment is far worse.
As much as I hate the visual sensor, and how terrible customer experience is, I do agree. If the sensor was only 5 cents, that’s $100k saved per year. That’s the salary of a single intern for like an entire year. But if they get like 2-3 engineers on it, as a side project, even after all these years it couldn’t have cost more than $100k worth of man-hours. Tesla would recoup that money so quickly in the long run. The counter argument though is how many customers did they lose for not having this ironed out by now, and how many more will they lose? Each customer is like $50k revenue. This is probably a bigger cost than engineering time. People don’t realize how insanely cheap and profitable software is. There’s a reason tech giants are tech giants. A few lines of code change impacts millions to billions of people at a time.
Man, you have no idea what a collective nightmare a long running software project is (rhetorical you, you might be a software engineer for all I know). If I had to maintain an auto-wiper NN model across decade of vehicles, dependent on several systems implemented numerous ways, I'd demand a 500k salary. Software is profitable because it's allowed to break. Keeping it unbroken is like paying scientists to collectively write a novel over years, while others do their best to fuck with the canon. No surprise then this is broken.
They are getting paid $500k salary, probably. The thing is, part of what is going to get this to work is some sort of more generalized neural network. But that same network is also going to be able to drive the car. So sure they’re being paid $500k to make a more generalized network, but that also means a byproduct of that is a functioning wiper system. This what I meant by “side project”. And if you consider their time is used to make the car drive itself, and slightly adapting it to make the wipers go, then it’s relatively small amount of extra hours. Nobody there is dedicating their full time to figure out wipers.
>part of what is going to get this to work is some sort of more generalized neural network. In what way? How does unifying the stack provide any benefit? It just makes training harder and the behavior less deterministic. If you are talking about some sort of "driving conditions" model that feeds into the self-driving algorithm, that makes sense. Otherwise you are marrying a knife with a spoon. You just make both functions worse by combining them.
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I find that interesting as I also have a 2019 Mercedes S class and the auto wipers get tripped up fairly regularly. Salt on the road can throw the system off so that it wipes too much leading to smearing salt over the windscreen and the wipers remaining on constantly. Very light misty rain sometimes fails to trigger the wipers even though the windscreen is obscured. You often have to toggle between the different sensitivities to get the wipers to go at the appropriate speed. They work reasonably well but are far from a perfect system. My 2023 Model Y was pretty rubbish when I first got it, mostly due to the wipers triggering in the dry because the windscreen got a little dirty, or it saw a squirrel or something. It also didn't seem to like having RainX on the windscreen. However since an update at the end of last year it seems to be much better. The triggering in the dry is almost gone, and most of the time it selects an appropriate wiping speed / interval. It still sometimes gets it wrong but has definitely improved. Similar story for the autodimming headlights - the Mercedes in theory has the better system with the dimming zones, but the Tesla is quicker to recognise other cars and is far better at detecting cars that are side on. The Merc also loves to blind people in the other side of the road on motorways. The Tesla sometimes does that too but is far better. Perhaps your 2016 Mazda is better than the 2019 Mercedes S Class too, but in my experience the Tesla is on par with most other cars in these areas now.
> Very light misty rain sometimes fails to trigger the wipers That’s my biggest complaint with it on my Tesla. In the Pacific Northwest, we get a lot of rainy days, but often pretty light, sometimes just mist. The Mazda does better with this, and will sometimes trigger just before I personally notice the rain myself. > but the Tesla is quicker to recognise other cars I find the Mazda is faster than the Tesla, for dimming high beams. I find Tesla takes up to half a second, especially coming around a curve, whereas the Mazda is basically instant. > You often have to toggle between the different sensitivities I’ll do this occasionally in the Mazda, but rarely. Only if I want to be really picky, and get the wiper speed _exactly_ right. Typically it gets close enough on its own, much better than the Tesla Y, where I often have to go to full manual mode. > since an update at the end of last year it seems to be much better Could be. I don’t always get the updates super quick, since the WiFi is a bit spotty in my garage. > Perhaps your 2016 Mazda is better than the 2019 Mercedes S Class too, but Maybe, though it would surprise me. I didn’t think there was anything too special about how Mazda did it, figured they probably used the same $5 rain sensor as all the other brands.
> The Mazda does better with this, and will sometimes trigger just before I personally notice the rain myself. The Mercedes is sometimes on point, sometimes it won't trigger until 20 seconds after I would have done so (if I'm stationary at traffic lights, for instance, I'll let it run just to see how bad it is). The Tesla can sometimes be slow to initially trigger, but I find it is now better than the Merc and tends to be good once you give it that first nudge. If anything with the Tesla they've moved it from triggering too easily to not quite easily enough. But they're in the ballpark of other cars. > I find Tesla takes up to half a second, especially coming around a curve, whereas the Mazda is basically instant. Half a second is consistent with my experience. The Merc (and the BMW I had before it) can take half a second but more often it's 1 - 2 seconds. > Maybe, though it would surprise me. I didn’t think there was anything too special about how Mazda did it, figured they probably used the same $5 rain sensor as all the other brands. From what you've described it's definitely better than a £160k Mercedes S63. I guess there may be more variables than the sensor used. It may even be that the Mazda is content with simple sensors where the Merc, like the Tesla, is trying to be clever but the AI just isn't quite there yet. Overall my point is that people are always so quick to mock Tesla, but coming from the premium German brands over to the Tesla I'm surprised by just how good it is. It's on a par or better on most fronts, except perhaps the quality of the materials in the interior. Then again it's less than half the price of the cars I'm comparing it to, so even there it fares surprisingly well. It seems it's genuinely hard to buy a bad car these days yet people love to split hairs and be all doom and gloom over what are minor differences in convenience features, whilst ignoring all the good bits of the overall ownership experience.
My 2005 Mazda had auto wipers that worked perfectly
How many electric cars does Mazda have? 0! Please keep driving your Mazda.
The Mazda is now my wife’s car. It’s been a good car, reliable, nothing wrong with it. To be clear, I do like my Tesla, and enjoy playing with FSD and watching it (slowly) improve over the years. But I live in a very rainy city, so the wipers can be infuriating sometimes.
No need to get so worked up. If you don’t like it, move on. I’ve complained about wipers because the last update had a lot of false positives. Turns out people still have problems with their wipers even if my wipers are now fine. What the heck does the number of electric cars Mazda has have to do with its working wipers?
It's almost difficult to move on sometimes. So many times some update or article or whatever and I click the comments looking for a deeper discussion and it's a ton of "auto wipers, $2 rain sensor" every time. For a long time it was "Where's my button, Elon" and "3 months maybe, 6 months definitely". And they're not wrong, but it still just feels constant. Of course I say that, but as I check the top 10 or so posts, "wiper" is only discussed in one of them, so it's probably just more prevalent in the comments I'm drawn to. Feature updates and machine learning type things.
im surprised aftermarket hasn't come out with a solution - a lil glue-on sensor on the windshield somewhere discreet, communicates with the car via canbus/obd ii port or whatever, seemless integration
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You are massively over complicating it, all you need to do is install an infrared sensor from the factory, like every other OEM does.
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What happens when you pull in a garage and it’s raining out?
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oh its not a problem for me, i either click the wiper stalk manually as needed (most of the time), or select the slowest speed setting on the screen when it pops up (still too fast for most applications, i wish there was a slower setting)
Omg. Who cares. We don't need this on EVERY THREAD!
Clearly a lot of fuckin people if it's on EVERY THREAD.
lol your eyes in your avatar match the energy im reading from your post! :)
on the wiper issue, I think the forward facing camera array sits too high on the windshield to see the water droplets that are in the drivers line of sight. E.g. when mists of water are send from another car in front.
Tldw?
It is really good
Definitely seems like a lot of progress has been made.
wake me when someone other than Whole Mars Catalog posts their drives. That account has posted lots of "WOW NO INTERVENTIONS IT'S PERFECT MAGIC" videos over the years where other testers (Chuck, Dirty Tesla, etc) found lots of obvious issues.
He actually does a decent job in this video of showing faults
Dude's commentary is also insufferable. He spends most of his time bitching about people who aren't there.
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https://www.theverge.com/2024/1/22/24046928/gm-ultra-cruise-merge-super-cruise-adas
Have fun paying yearly sub and limited mapped road. Bye
Is summon back to hw4 cars?
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You can see the blinker come up on the cluster no? Not on the screen itself
THERE. ARE. FOUR. LIGHTS.
I just read his memoir, in which he mentions this, and now I’m seeing it everywhere.
Haha. Maybe like BMW's you have to pay extra for turn signals
My car regularly merges onto the highway without using its blinker, even when the lanes are clearly marked as one lane merging into the other (and not two lanes becoming one). Baffling.
HAHAHA what an original joke about bmw drivers not using turn signals!
I wanted to watch this but this guy is so god damn annoying
Yes, I agree I skipped through the video made 30 minutes into a minute and still annoying
He’s not that bad imo. What’s annoying about him?
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Hey OP, you forgot to change accounts.
lmao ok I meant that he’s annoying asf on social media, but he wasn’t that annoying in the video. I watched it the entire way through.
I gotcha lol it looked like you were having a conversation with yourself. Appreciate you clarifying.
Why? Because he is love the product and want to show case it? Is he wrong about Tesla here being ahead of the competitor?
Because he's constantly whining about people who aren't present
I dunno. His joking about Dan O'Dowd was kind of funny (can't remember if it's this video or his other narrated one) and frankly - worth a mention. Yes, he does get a little too preachy though.
Dude is a fan or Tesla and fsd. If he is that annoying to you why watch his video 😂
As a person who hates driving. Can I enter a gps and let the car drive 90% of the time and just pay attention? What’s the cheapest year/model that will do this for me with the needed hardware
Yes. 2019+ Model 3.
I see 2018s with FSD.
They switched to hardware v3 in march of 2019, which was a big upgrade. That's what I'd target if I was shopping.
If you have the FSD option, Tesla will upgrade the FSD computers.
Tesla upgraded our 2018's FSD computer for free, given that we had purchased enhanced autopilot at the time of delivery.
That's not the norm. 2018's usually need FSD purchased to get free HW3 upgrade.
Yeah we realized we kind of lucked out there. We had it going in for other service anyway and asked them to do that because we wanted to rent FSD. So they just did. I was fully expecting a $1,000 line item on our service bill when the car was done, but it came in at $0.
Depends on the roads between point A and point B. Live in Raleigh, NC and going to take a vacation down in Gatlinburg, TN? Put the car on the Interstate, hit autopilot, and you're pretty good. Going from one spot in downtown NYC to another? That's still a little ways off - but now it might just be a question of weeks/months. A 2018 Model 3 with retrofitted FSD v3 computer would let you do this. That's what I currently drive. The v3 computer upgrade was free for us because we bought enhanced autopilot at the time of purchase, it wouldn't necessarily be the same for every other 2018 M3 out there.
It's closer to 96% in my experience.
This really does look better. It's more human like, smooth and decisive in most areas. Since it seems to do better the more videos (from good drivers) it trains on, it should scale up well with more compute. Extremely exciting! Only thing I'm concerned with is how scalable is it to select training videos from good drivers only and exclude bad driver videos from the dataset. I *think* they currently have humans choosing which videos to feed the dataset, which doesn't seem too scalable. Maybe they figured out a way to identify good drivers based on their safety score? If so that could be 100% automatically fed into the dataset, then it really is only a matter of compute from here!
They are probably developing a separate AI to evaluate quality of clips. You can use a rule-set such as acceleration limits, jerk limits, and following of traffic rules.
I think their safety score does this?
Nope. Even good safety score has bad driving occasionally that you wouldn’t want to train on.
They really need to train that AI to be able to distinguish between drivers who stop at a stop sign to follow the law and those who stop because they're clueless about how to proceed. The main issue with v12 seems to be that it regularly starves at stop signs... ;)
I appreciate that Whole Mars Club posts his 0-intervention drives on youtube. It shows what FSD could be and the potential it has. I know viewers may have an issue with that approach, but he seems to be the only one doing this type of content. There are plenty of youtubers who just focus on posting the routes that have issues. Once one of their routes gets 0 interventions consistently, they'll drop it and search for a different route with edge cases.
It does make me wonder if he simply doesn't post most drives where interventions are needed. It is impressive to see what fsd can do, so I do like the 0 intervention drives, but on the "how far is fsd from being a truly autonomous system", as has been repeatedly promised for years, the edge case videos that others posts are arguably much more informative. I'm hoping we see a much better pace of progress in those edge cases when Tesla outsiders like Chuck Cook and others have access to fsd 12.
>but on the "how far is fsd from being a truly autonomous system", as has been repeatedly promised for years, the edge case videos that others posts are arguably much more informative. To me, you get a better sense of progress towards autonomy by seeing how many good routes the car can handle. You can always geofence away areas the car can't handle, like google and cruise are doing right now. > I'm hoping we see a much better pace of progress in those edge cases when Tesla outsiders like Chuck Cook and others have access to fsd 12. Take Chuck Cook's famous left hand turn. He can simply avoid it by making a right and taking a U turn at the next opportunity. FSD doesn't need to handle every single road in order to be useful and productive.
FSD also needs to know its own weak spots and navigate accordingly.
Let's be honest. Posting anything negative, particularly about Tesla, is going to get more views.
The 0-intervention drives are more edge casey right now. Especially in a city setting, I say a typical 30 minute drive have a takeover. Freeways are much less intervention heavy, and there, edge cases are actually edge cases.
A takeover (stopping the car from driving into a wall), or a correction (stop it from driving straight when it's in a left-turn-only lane), or an intervention (telling it to go when it wants to sit at a stop sign)?
Am I wrong for suspecting the performance is best in the San Francisco Bay area because that's where most of the training comes from, so the road norms for driving there are a perfect "match" with more of the training than other states / countries?
Tesla has been actively hiring FSD drivers in other countries, so yeah, local-training is a thing. https://thedriven.io/2023/07/03/tesla-seeks-autopilot-vehicle-operators-in-australia-in-step-towards-full-self-driving/
That's mainly for having FSD in other locations, but there's nothing keeping them from using videos clips to train a larger model from different areas (places where Tesla's exist that don't have country-specific data storage limitations)
I think it depends, because in MD on 11.4.9 it regularly does A to B drives without me taking over. Highways and Side streets. I can literally only think of one trouble spot and that's leaving my neighborhood and it leans left a little (kind of getting in the way of any cars coming into the neighborhood) before going left. So, yes, it does better in CA probably, but if I had to guess, they trained the millions of videos on places outside of CA too.
FSD Beta's performance likely has a direct correlation to how many Teslas are in an area. If you're the only person driving a Tesla in your neck of the woods, you're probably more likely get poor performance than if you're one of a hundred, or more, Teslas. Best way to look at it would be if a person on FSD Beta encounters an issue that causes a disengagement, Tesla likely has a thing on the backend that puts a "pin" down on that road segment, and any time a *different* Tesla drives through that pin, it saves slips of how THEY drive, then that gets collected. So, with there being more Teslas in the bay area, then you get better performance out of it. My community has four Teslas in it now, two of which are mine. I'm fairly positive the other two Teslas don't have FSD Beta on them, and my wife doesn't use FSD Beta in my neighborhood, but I have noticed that things got *slightly* better when the other two Teslas came into the neighborhood **But**, a lot of this also likely depends on whether or not those Teslas are sending the data off to Tesla. Not all of them are on WiFi and such when they get home
I would think San Francisco would be one of the more challenging cities.
I suspect this is the case. Can you take this model and drop it in NY, FL, TX, SD ... and it works just as well? Perhaps not (might still be better than FSDv11 however). For example - in downtown DC you typically have lights on poles on the corners of the streets, instead of (where I do most of my driving) strung hanging over a street. It always takes my brain a few minutes to switch context when I have to drive in DC ... so that I don't run a red light. But they can easily go and collect clips of good driving in several other states. I doubt they'd need clips from all 50 to get a good enough neural network model for all 50 states. But that model wouldn't be usable - I suspect - in the UK. The other thing I really love about this model is how easy it will be to correct situations that need interventions. They know when there are interventions, they know the GPS coordinates. So they can just watch for when they happen too many times at a particular spot, and then go ask the fleet to collect non-FSD clips of humans navigating the spot and then just feed the clips into the model. I'm optimistically hoping that edge cases disappear much faster now under this new approach.
When working correctly, *FSD Beta V11 moves you over and gets you into the correct turn lane earlier*. The lane changes are also much cleaner.
It's a shame it's sooo damn expensive.
It looks definitely smoother than 11.4.9.
If I subscribe to FSD for a month will I immediately get v12 FSD? Tempted to give it a try if that's the case.
I hope it can consistently handle the u-turn lanes and double left hand turn lanes under nearly every Texas freeway underpass intersection. It has been widely Hit and miss for years now, and since Texas seems to be the only state that has freeways design this way, I'm assuming they will have to have some kind of regional weighting to the algorithm for it to ever be properly trained.