451–460 of 483 posts
I can see a path where with only cameras, Tesla might be able to reach level 4 autonomy in perfect conditions. But the biggest thing that comes to mind is what happens at night. Are they only going to enable self-driving during the day?
Wouldn’t turning on the headlights fix the problem at night? Snow and ice may be another challenge but night sounds easy.
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See my sibling comment, but I agree with you, especially about Lex's track record, and I think the other commenters are projecting. And you're right, reframing a question is like THE MOVE for academics, so it's completely not evidence that Andrej was making "a bullshit move" (although it's also a classic move for PR flaks, so it's possible he was making a bullshit move).
i looked at your account and, were you ever involved in real estate?
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Uh, that's not at all a good paraphrase. Q: "Does [removing some sensors] make the perception problem harder, or easier?" (note, this is literally what Lex asked, your restatement is misleading) A: [paraphrasing] "Well more sensor diversity makes it harder to focus on the thing that I believe really moves the needle, so by narrowing the space of consideration, I think we'll get better results" Karpathy might not be t…
> because it's often true that you can deliver better by focusing on a smaller number of things. This is true / dogma in linear / non-linear regression world, but of no real import in deep learning or Bayesian methods.
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Source: have worked with some of the (admittedly last-gen) automotive RADAR chips, NXP in particular. The issue is the number of false positives, stationary objects need to be filtered out. Something like a drainage grill on the street generates extremely strong returns. RADAR isn't high enough resolution to differentiate the size of something, you only have ~10 degree resolution, and after that you need to go by str…
Hi, thanks. Thought it might be so. Still... RADAR can have high(er) angular resolution with (e.g.) phased arrays (linear or not) and digital beamforming. I guess it's the way the industry works and it wants small cheap composable parts, but using the full width of the car for a sensor array you could get amazing angular accuracy, even with cheap simple antennas. MIMO is also supposed to give somewhat better angular…
Which car brand do you think would take up these restrictions, and which customer is then going to buy the car with the big ugly patch on the front?
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Hi, thanks. Thought it might be so. Still... RADAR can have high(er) angular resolution with (e.g.) phased arrays (linear or not) and digital beamforming. I guess it's the way the industry works and it wants small cheap composable parts, but using the full width of the car for a sensor array you could get amazing angular accuracy, even with cheap simple antennas. MIMO is also supposed to give somewhat better angular…
There's also a legitimate harm to consumers with such a large radar array in the front bumper. Because even a minor fender bender could total a $50k car. So the car would be very difficult to sell since few people are willing to pay much higher insurance premiums just for that.
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Maybe in US. Tesla has nothing to throw against some serious cars like Mercedes EQS or EQS SUV. Hyundai is also catching up. Charging infrastructure isn’t an issue in Europe anymore and is getting better and better every year. Tesla supercharger for free at the beginning was a big deal. That’s why I am looking for used model X from 2017 right now. 5000€ in free electricity a year is a nice advantage. I also have open…
> Tesla has nothing to throw against some serious cars like Mercedes EQS or EQS SUV. Besides sensationalist comments, do you actually have a list of what areas the Mercedes excel that Tesla doesn't? I can't take "serious car" seriously - like what, Tesla is kidding?
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> Tesla has nothing to throw against some serious cars like Mercedes EQS or EQS SUV. Besides sensationalist comments, do you actually have a list of what areas the Mercedes excel that Tesla doesn't? I can't take "serious car" seriously - like what, Tesla is kidding?
Let’s talk about my future Model X 2017 with free supercharging. The chassis is disaster from beginning to the end. The car has air suspension, it’s nice. But! You go with low setting, tires don’t last 10k miles. You go with high settings, axles are suddenly broken. After 60000 miles all parts under the car must be replaced. Not funny and not cheap either. Last car I heard of such nonsense was 25 years old Renault La…
It's well known that EVs in general due to their weight, and especially those with high torque cause the tyres to wear out much faster than their ICE counteparts. 10K miles sounds too low. Would love to know why low suspension setting on a Model X causes much faster wear, and whether for the same efficiency/range on a different vehicle, the tyres last significantly longer.
> You go with high settings, axles are suddenly broken.
We know media is trigger happy when it comes to the most minor Tesla related incidents and yet, I've never, in the 10 years of following Tesla, heard about this one. Can you link to perhaps a bunch of these?
> After 60000 miles all parts under the car must be replaced.
Again, never heard of these issues and I watch daily Tesla news from multiple sources.
> Let’s talk about my future Model X 2017
I'm puzzled. Despite all the issues you listed, you're still going with Model X just because it has free supercharging? You do realize that broken axels can kill you, right?
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Hi, thanks. Thought it might be so. Still... RADAR can have high(er) angular resolution with (e.g.) phased arrays (linear or not) and digital beamforming. I guess it's the way the industry works and it wants small cheap composable parts, but using the full width of the car for a sensor array you could get amazing angular accuracy, even with cheap simple antennas. MIMO is also supposed to give somewhat better angular…
In theory a big phased array of cheap antennas is cheap, in practise not because you need to have equal impedance routing to all of the antennas, which means you need them all to be roughly equidistant to the amplifier. You could probably get away with blowing it up to the size of a large dinner plate, but then you also need a super stiff substrate to avoid flexing, and you need to convince manufacturers that they sh…
Modern phased arrays can have independent transmitters (synchronized digitally or with digital signal distribution) or you can have one 'cheap and stupid' transmitter and many receivers, doing rx beamforming, and as for complexity you mostly 'just' need to synchronize them (precisely). The receivers can then be made on the very cheap and you need some signal distribution for a central signal processor.
Non-linear or sparse arrays are also now doable (if a bit tricky to calibrate) and remove the need for complete array or rigid substrate or structure.
If you imagine the car as a multistatic many-small-antennas system there's lots that could be done. Exploding the RADAR 'box' into its parts might make it all far more interesting.
I'll admit I'm way over my head on the industrial aspects, so thanks for the reality check. Just enthusiastic, the underlying radar tech has really matured but it's not easy to use if you still think of the radar as one box.
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> There are trade offs because while you gain different views of the environment from different sensors, the fusion becomes more complicated and has to be sorted out in software reliably in real-time. If you're against having multiple sensors though, the rational conclusion would be to just have one sensor, but Tesla would be the first to tell you that one of the advantages their cars have over human drivers is they…
This is one side of the argument. The other side of the argument is that what matters more than the raw sensor data is constructing an accurate representation of the actual 3D environment. So an argument could be made (which is what this guy and Tesla are gambling on and have designed the company around), is that the the construction & training of the Neural out-weighs the importance of the actual sensor inputs. In t…
Yes, in machine learning, pruning down to higher signal data is important, but good models are absolutely amazing at extracting meaningful information from noisy and diffuse data; it's highly unusual to find that you want to dismiss a whole domain of sensor data. In the cases where one might do that, it tends to be only AFTER achieving a successful model that you can be confident that is the right choice.
Tesla's goal is self-driving that consumers can afford, and I think in that sense they may well be making the right trade-offs, because a full sensor package would substantially add to the costs of a car. Even if you get it working, most people wouldn't be able to afford it, which means they're no closer to their goal.
However, I think for the rest of the world, the priority is something that is deemed "safe enough", and in that sense, it seems very unlikely (more specifically, we're lacking the tell tale evidence you'd want) that we're at all close to the point where you wouldn't be safer if you had a better sensor package. That means, in effect, they're effective sacrificing lives (both in terms of risk and time) in order to cut costs. Generally when companies do that, it ends in law suits.
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I don't think he's lying. I think he's looking at things from a particular perspective, and that perspective is being critiqued, as it should be.
True, he could be wildly misled but he's been around doing this for a while, so that seems unlikely. He could be truly delusional in either case but it's still kinda necessary to knock down his arguments or logic; and that's the critique or analysis I'm not seeing. Just assertions again and again that the real reason is economics triumphing over safety. I'm beginning to think that the idea that there are genuine trad…
Yes, with ML models, you often can be better off trimming down your sensor data. Usually though, you don't remove entire categories of sensor data. Even when you do, to be confident in such a move, you need to first achieve a working model with whatever data you have, and then through refinement you can prove that whole categories of data are more hinderance than help. They haven't done that.
It seems quite clear that the reality is a full sensor array is just economically non-viable with their business model, and that's framing their whole thought process.
Because they promised FSD back in 2017, they can't acknowledge that going without those sensors means it's going to take them much longer to achieve FSD. Because of safety/regulatory oversight, they also can't acknowledge that going without those sensors means there will be additional safety risk.
So they're stuck making these rationalizations that everyone in the industry knows are at best half-truths. No doubt, at some point we'll figure out how to do self driving with a much more limited sensor package, and when we do, we'll achieve a significant improvement in the cost effectiveness of self-driving.
In the meantime, there's a lot of "rationalization" going on.