Q: "are less sensors less safe/effective?"

A: "well more sensors are costly to the organization and add more tech debt so safety is orthogonal and not worth answering".

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 telling the truth, I don't know. But it's a much more credible pitch than you make it sound, because it's often true that you can deliver better by focusing on a smaller number of things. Engineering has always been about tradeoffs. Nobody is offering Karpathy infinite money plus infinite resources plus infinite time to do the job.

Again, I'm not saying Karpathy is honest or correct. I'm saying that the rephrasings in this comment and this thread are hilariously unfair.

Our brains do an amazing job interpreting high resolution visual data and analyzing it both spatially and temporally. Our brains then take that first analysis and apply a secondary, experiential, analysis to further interpret it into various categories relevant to the current activity.

What I’ve seen from Tesla so far indicates to me that FSD shouldn’t be enabled regardless of what sensor package they’re using, let alone based on camera data only. They need to solve their ability to accurately observes their surroundings first, especially temporally. Things shouldn’t be flashing in and out that have been clearly visible to the human eye the entire time. Additionally, this all ignores the experiential portion of driving. When most people approach something like a blind driveway or crosswalk obscured by a vehicle (a dynamic, unmapped, situation), they pay special attention and sometimes change their driving behavior.

This is true / dogma in linear / non-linear regression world, but of no real import in deep learning or Bayesian methods.

My understanding is that by focusing on fewer things (vision only), they bet to make progress faster because of the simplified organisational aspect.

In general the ‘harm to consumers’ is really just making it more likely they damage the car in a parking lot or their garage, which tells you where their priorities are (sales, Automotive gross profit). Assuming occupancy network works, the only real blind spot left is if something in front of the car changes in between it turning off and on (assuming occupancy will 'remember' the map around it when it goes to sleep).

Also, Tesla’s strategy for safety is seemingly “excel in industry standard tests, ie. IIHS and EuroNCAP”, so this might be a case of the measure becoming a target.

Meanwhile, radar is the principal sensor used in systems like automatic emergency braking across the industry. It has no intersection with any of the parking stuff because it generally has to ignore stationary objects to be useful (hence the whole "Teslas crashing full speed into stopped vehicles" thing).

That's literally trivial for a car with radar to detect.

Amazing how people talk about stuff they have no idea about when it comes to Tesla.

That's literally trivial for a car with radar to detect.”

That crash occurred on a car which was using radar. Automotive radar generally doesn’t help to detect stationary objects.

Further, that crash occurred on a vehicle with the original autopilot version (AP1), which was based on Mobileye technology with Tesla’s autopilot software layered on top. Detection capabilities would have been similar to any vehicle using Mobileye for AEB at the time.

Maybe it's difficult for reasons of false alarm detection (too many stationary objects that are not of interest) but you can get very good results with tracking (curious about these radars' refresh rate), STAP, and classification/identification algorithms, especially if you have a somewhat modern beamformed signal (so, some kind instant spatial information). Active-tracking can also be of help here if you can beamsteer (put more energy, more waveform diversity on the target, increase the refresh rate). Can't these radars do any of those 'state of the art 20 years ago' stuff?

There's something I don't get here and I feel I need some education...

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 strength of the returned signal. So there's no way to differentiate a bridge girder or a railing or a handful of loose change on the road from a stationary vehicle. On the other hand, if you have a moving object, RADAR is really good at identifying it and doing adaptive cruise control etc.

Edit: it looks like some of the latest Bosch systems have much better performance in terms of resolution and separability: https://www.bosch-mobility-solutions.com/media/global/produc...

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 accuracy, since you can perform actual monopulse angular measurement (as if you had several independent antennas). There's even recent work on instant angular speed measurement through interferometry if you have the original signals from your array.

And with the wavelengths used in car RADARs you could get far down on range resolution, especially with the recent progress on ADCs and antenna tech.

I'm not saying you're wrong, you're describing what's available today (thanks for that).

Wondering when all this (not so new) tech might trickle down to the automotive industry... And whether there's interest (looking at big fancy manufacturers forgoing radar isn't encouraging there).

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?

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.

If you wanted separated components to group together many antennas I suspect the difficulty would be accurate clock synchronization what with automotive standards for wiring. I'm still not sure I understand how they can get away without having rigid structures for the antennas, but this would be a critical requirement because automotive frames flex during normal operation.

Cars are also quite noisy RF environments due to spark plugs.

I guess what you're speaking of will be the next 10-20 years of progress for RADAR systems as the engineering problems get chipped away at one at a time.

Thanks for humouring me. RADAR is a very fun and interesting topic.

So the car would be very difficult to sell since few people are willing to pay much higher insurance premiums just for that.

Personally I've never seen these claims come from the mouth of an automotive radar expert, and many cars do use radar in their adaptive cruise control, so I present it as a rumour, not a fact :)

I’m in the same boat as to not understanding why, but from what I have read the problem indeed isn’t that it doesn’t detect them, it’s that there are too many of them, and nobody has figured out how to filter out the 99+% of signals you have to ignore from the ones that may pose a risk, if it’s doable at all.

I think that at last part of the reason is that spatial resolution of radar isn’t great, making it hard to discriminate between stationary objects in your path and those close to it (parked cars, traffic signs, etc). Also, some small objects in your path that should be ignored such as soda cans with just the ‘right’ orientation can have large radar reflections.

Some of the newest car radars can do some beam formimg, but not all.

Most models have multiple radars pointing in multiple directions as that's cheaper than AESA.

Only just recently have "affordable" beamformer's come to the market. And those target 5G basestations.

So the spec in most K/Ka-band models starts at 24.250GHz, where the 5G band starts. While the licence free 24GHz band that the radars use is 24.000-24.250GHz.

If this was not bad enough there has been consistent push from regulators to get the car radars on the less congested 77GHz band. And there's even less afforable beamformers for that band.

Now, where we disagree is you implying that cars with AEB-level radar (literally $10 off-the-shelf parts with whatever sensor fusion some MobilEye intern dreams ups) are somehow the same as self-driving cars (the goal of Tesla Autopilot).

Every serious self-driving car/tractor-trailer out there uses radar as a component of its sensor stack because Lidar and simple imaging is not sufficient.

And that's the point I was trying to make - we agree it's trivial for radar to find things they just need sensor fusion to confirm the finding and begin motion planning. This is why a real driverless car is hard despite what Elon would like you to believe. There is no one sensor that will do it. Full stop.

And this cuts to the core of why Tesla is so dangerous. They are making a car with AEB and lane-keeping and moving the goal posts to make people (you included) think that's somehow a sane approach to driverless cars.

Yet somehow, humans can drive cars with just a pair of optical sensors (mounted on a swivelling gimbal, of sorts).

In theory, a sufficiently capable AI should be able to drive a car at least as well as a human can using the same input: vision.

A pair of optical sensors and a compute engine vastly superior to anything that we will have in the near future for self-driving cars.

Humans can do fine with driving on just a couple of cameras because we have an excellent mental model (at least when not distracted, tired, drunk, etc.). Cars won't have that solid of a mental model for a long, long time, so sensor superiority is a way to compensate for that.

When we look at the road, we recognize stuff in the images we get as objects, and then most of the work is done by us applying basic logic in terms of those objects - that car is off the side of the road so it's stationary; that color change is due to a police light, not a change in the composition of objects; that small blob is a normal-size far-away car, not a small and near car; that thing on the road is a shadow, not a car, since I can tell that the overpass is casting it and it aligns with other shadows.

All of these things are not relying on optics for interpreting the received image (though effects such as parallax do play a role as well, it is actually quite minimal), they are interpreting the image at a slightly higher level of abstraction by applying some assumptions and heuristics that evolution has "found".

Without these assumptions, there simply isn't enough information in an image, even with the best possible camera, to interpret the needed details.

Of course, and all this is exactly what self-driving AIs are attempting to implement. Things like object recognition and understanding basic physics are already well-solved problems. Higher-level problem-solving and reasoning about / predicting behaviour of the objects you can see is harder, but (presumably) AI will get there some day.

Basically my contention is that vision-only is being touted as the more focused path to self-driving, when in fact vision-only clearly requires a big portion at least of an AGI. I think it's pretty clear this currently means this is not a realistic path to self-driving, while other paths to self-driving using more specialized sensors seem more likely to bear fruit in the near term.

And Tesla lacks that, so therefore they ought not simply rely on cameras and ought use extra auxiliary systems to avoid danger to their consumers, they are not doing this because it reduces their profit margins, alas, this hn thread

In fairness, humans have a lot more than just optical sensors at their disposal, and are pretty terrible drivers. We've added all kinds of safety features to cars and roads to try to compensate for their weaknesses, and it certainly helps, but they still make mistakes with alarming regularity, and they crash all the time.

When you have a human driver, conversations about safety and sensor information seem so straightforward. The idea of a car maker saving a buck by foregoing some tool or technology at the expense of safety is largely a non-starter.

What's weird is, with a computer driver, (which has unique advantages and disadvantages as compared to a human driver) the conversation is somehow entirely different.

This is a super important point. Whenever self-driving cars comes up in conversation it's like, "we're spending billions of dollars on self-driving cars tech, but what if we just, idk, had rails instead of roads". We're putting all the complexity on the self-driving tech, but it seems pretty clear that if we helped a little on the other end (made driving easier for computers), everything would get better a lot faster.

In theory, cars should be use mechanical legs instead of wheels for transportation, that's how animals do it. In theory, plane wings should flap around, that's the way birds do it. My point being: the way biology solved something may not always be the best way to do it with technology.

Wheels and legs solve different problems. Wheels aren’t very useful without perfectly smooth surfaces to run them on. If roads were a natural phenomenon that had existed millions of years ago, then isn’t it plausible that some animals might have evolved wheels to move around faster and more efficiently?

This is wrong and I was surprised to hear them say it was enough in the video.

We don't have car horns and sirens for your eyes. You will often hear something long before you see it. This is important for emergency vehicles. Once you hear it, a good driver will immediately slow down and pull to the side, or delay movement to give space for the vehicle.

Does this mean self driving vehicles can't detect emergency vehicles until they appear on camera? That's not encouraging.

Robotically performing an action in response to single/few stimuli with little consideration for the rest of the setting and whether other responses could yield more optimal results precludes one from ever being a "good" driver IMO.

"See lights, pull over" is not going to cut it. See any low effort "idiot drivers and emergency vehicles" type youtube compilation for examples of why these sorts of approaches fall short.

Our eyes provide distance sensing through focusing, the difference in angle of your two eyes looking at a distant object, and other inputs, as well as having incredible range of sensitivity, including a special high contrast mode just for night driving. This incredibly, literally unmatched camera subsystem is then fed into the single best future prediction machine that has ever existed. This machine has a powerful understanding of what things are (classification) and how the world works (simulation) and even physics. This system works to predict and respond to future, currently unseen dangers, and also pick out fast moving objects.

Two off the shelf digital image sensors WILL NEVER REPLACE ALL OF THAT. There's literally not enough input. Binocular "vision" with shitty digital image sensors is not enough.

Humans are stupidly good at driving. Pretty much the only serious accidents nowadays are ones where people turn off some of their sensors (look away from the road at something else, or drugs and alcohol) or turn off their brain (distractions, drugs and alcohol, and sleeping at the wheel).

That number is probably too high for robots to do though.

Humans are weird like that.

Needs emphasis on can

  That's literally trivial for a car with radar to detect.

Asking this because I know first hand that the adaptive cruise function in my car must have a moving vehicle in front of it for the adaptive aspect to work. It will not detect a vehicle that is already stopped.

The resolution of the radar is pretty good though, even if the vehicle in the front is just merely creeping off breaks… it does get detected if it is at or more than the “cruising distance” set up initially.

The AEB function on my car depends on the camera.

Radar does however have the advantage of measuring object speed directly via the doppler effect, so you can filter out all stationary objects reliably, then assume that all moving objects are on the road in front of you and need to be reacted/responded to.

So I think it's the case that radar can detect stationary objects easily, but cannot determine their position enough to be useful, hence in practice stationary objects are ignored.

Radar is quite good at finding stationary metal objects, particularly. Putting it in a car, if anything, helps, because the station objects are more likely to be moving relative to the car...

The car that crashed had radar, vision, USS, AND it was based on another company's technology.

The sensors are unreliable and expensive in terms of R&D. Having marginal parts which takes money from a finite R&D budget can easily result in a worse product. “They contribute noise and entropy into everything.” … “you’re investing fully into that [vision] and you can make that extremely good. You only have a finite amount of spend of focus across different facets of the system.”

His standpoint can be summed up as “I think some of the other companies are going to drop it.” Which would be really interesting if true.

"Less sensors can be more safe/effective if that allows us to focus on making effective use of the sensor information we do have, which is the result we're aiming for with this descision." would be a reasonable answer (if true), but that doesn't seem like a fair interpretation of what he actually said.

“Organizationally it can be very distracting. If all you want to get to work is vision resources are on it and you’re actually making forward progress. That is the sensor with the most bandwidth the most constraints and you’re investing fully into that and you can make that extremely good. You only have a finite amount of spend of focus across different facets of the system.”

Which was from this section: Q: “Is it more bloat in the data engine?”

“100%” (Q:“is it a distraction?”) “These sensors can change over time.” “Suddenly you need to worry about it. And they will have different distributions. They contribute noise and entropy into everything and they bloat stuff”.

Even earlier he says:

“These sensors aren’t free…” list of reasons including “you have to fuse them into the system in some way. So that like bloats the organization” “The cost is high and you’re not particularly seeing it if your just a computer vision engineer and I am just trying to improve my network.”

I listened to his answer three times and I’m not able to come up with a different interpretation than that.

Andrej eventually gets to it. But his first response was to evade. Lex is a skilled interviewer. By not letting him wriggle out of a difficult question we eventually got a substantive answer. But Andrej's first instinct was to evade. That's notable.

Otherwise I agree.

Fair enough. Seemingly practiced may be a better description. (I'm not super familiar with his work.)

This is what doesn't add up to me. Either a lot of that previous wonder-talk was actually a lie, or there's something else going on here.

Tesla dropped radar and ultrasonic due to supply shortages. Nothing to do with their AI being smart.

Many first-hand reports on Tesla fanboy forums on how no-radar and no-ultrasonic autopilot is far worse than with the sensors.

More programmers are like having more testicles, theoretically they should enable you to have more kids, but in practice bootleneck are elsewhere.

Both your and my reasoning by comparison is equally valid

I bring up the programmers working on a project example just to illustrate how more isn't always better even if it theoretically can be.

Your optical system can be good as heck till a bug hits it directly on the lense coving an important frontal area and make it behave weirdly.

In your metaphor it's like asking if you should have project managers as well as engineers on your project. And Tesla has decided that having only engineers allows them to focusing on having the best engineers. And they avoid the distraction of having to manage different types of employees.

Team focus on vision which is by far the highest accuracy and bandwidth sensor allows for a faster rate of safety innovation given a constant team size.

The bottom line seems to be that the part shortages would have slowed production and cost cutting. The rest of the story seems like a fable to me. It was pretty clear Tesla removed the radar because it couldn't get enough radars.

The interview didn't really impress me. I'm sure Andrej is bound by NDA and not wanting to sour his relationship with Tesla/Elon but a lot of the answers were weak. (On Tesla and some of the other topics, like AGI).

I also expect an automated system to be better than the poor human in the drivers seat.

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 have multiple cameras looking at the scene already.

You already have a sensor fusion problem. Certainly more sensors add some complexity to the problem. However, if you have one sensor that is uncertain about what it is seeing, having multiple other sensors, particularly ones with different modalities that might not have problems in the same circumstance, it sure makes it a lot easier to reliably get to a good answer in real-time. Sure, in unique circumstances, you could have increased confusion, but you're far more likely to have increased clarity.

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.

More or less. You can take that decision on other grounds - e.g. "what would be safest to do if one of them is wrong and i don't know which one?"

The system is not making a choice between two sensors, but determining a way to act given unreliable/contradictory information. If both sensors allow for going to the emergency lane and stopping, maybe that's the best thing to do.

Predictability especially around failure cases is a very important feature. Most human drivers have no idea about the failure modes of lidar/radar.

Lights?

Ultrasonics struggle in the rain, btw.

EDIT: Also I'm not quite positive why the image is so dark when I reverse at night. But it still is. The slope and surface of the driveway might have something to do with that... Still I wouldn't trust that camera. The ultrasonic sensors otoh seem to do a pretty good job. That's just my experience.

EDIT2: I love the Tesla btw. The ultrasonic sensors seem to work pretty reliably, they're pretty much their own system, the argument about complexity doesn't really seem to hold water and on the face of it the cameras won't easily replace them...

It has everything to do with cost cutting?

Okay? Tesla is a car company and they are absolutely trying to sell a cheaper car. That's obvious to anyone that's been in one.

"Both methods have trade-offs."

Right, isn't that why most other systems use both?

He's not conspiring to trick people per se but he's also not being super clear. His position obviously makes it difficult to answer this question. It's possible he really believes this is better but if he didn't he wouldn't exactly tell us something that makes him and his previous employer look bad. Also his belief here may or may not be correct.

Is it a coincidence that the technical stance changed at the same time when part shortages meant that cars could not be built and shipped because of shortages of radars?

More likely there was some brainstorming as a result of the shortages and the decision was made at that point to pursue an idea of removing the additional sensors and shipping vehicles without those. This external constraint makes believing the claims that this is actually all around better, while hearing some reports of increases in ghost braking (anecdotes) a little difficult. Not clear if there was enough data at that time to prove this and even Andrej himself sort of acknowledges that it's worse by some small delta (but has other advantages, well shipping cars comes to mind).

So yes, sensors have to be fused, it's complicated, it's not clear what the best combination of sensors is, the software might be larger with more moving parts, the ML model might not fit, a larger team is hard to manager, entropy - whatever. Still seems suspicious. Not sure what Tesla can do at this point to erase that, they can say whatever they want, we have no way of validating that.

Here is one possible perspective from an engineering standpoint:

Same amount of $$, same amount of software complexity, same size of engineering teams, same amount of engineering hours, same amount of moving parts. One company focuses on multiple different sensors and complex fusion with some reliance on AI. Another company focuses on limited sensors and more reliance on AI. Which is better? I don't think the answer is clear.

The other point is that I am arguing that many people are over-stating the importance of the sensors. They are important, but far more important is the post-processing. Any raw sensor data is a poor actual representation of the real environment. It is not about the sensors, but about everything else. The brain or the post-sensor processing is responsible for reconstructing an approximation of the environment. We have to infer from previous learned experiences of the 3D world to successfully navigate. There is no 3D information coming in from sensors, no objects, no motion, no corners, no shadows, no faces, etc. That is all constructed later. So whoever does a better job at the post-processing will probably out perform regardless of the choice of sensors.

Now, simple color matching models are used in some fancy toasters on white bread to determine brownness. That's the most I've ever seen in appliances...

Tesla has ca. 1000 software engineers working in various capacities. The ca. 300 that work on car firmware and autonomous driving are probably not participating in the Twitter drama.

https://www.bloomberg.com/news/articles/2022-10-27/tesla-eng...

And the corresponding HN thread: https://news.ycombinator.com/item?id=33365065

By hiding the ball that you are starting from a much more unsafe position

They are literally the least accurate of all sensors.

Radar tells you distance and velocity of each object. Lidar tells you size and distance of each object. Ultrasonic tells you distance. Cameras? They tell you nothing!

Everything has to be inferred. Have you tried image recognition algorythms? I can recognise a dog from 6 pixels, the image recognition needs hundreds, and has colossal failures.

We have no grip on the results AI will produce and no grasp on it's spectacular failures.

Driving will have to be solved without AI

You may be right about the actual decision process Tesla went through, but Karpathy is right in principle. One of the first things he says is "there can be problems with [the sensors]", and a lot of what he mentions increases the risk of run-time failure, not just cost.

It's easy to cast this as an optimization problem where you're trading off asymptotically improved sensing for linearly or superlinearly increased failure rates. There's certainly a point where the complexity of more sensors or certain types of sensors outweighs any marginal benefit they provide.

Cameras can also fail at run-time there can (and is) be variability in how they're mounted, in the lenses, in the sensors. They can get blinded or not get enough light. Their cabling can fail random components can fail.

Tesla has claimed that vision outperforms vision+radar but anecdotal reports don't seem to support that conclusion. IMHO these technologies are not directly replaceable, but are complementary. It's like you can't replace your ears with your eyes (yeah, you can read lips, if they're visible).

But sure, there is a sweet spot. Is Tesla really optimizing for best performance at any cost or are they optimizing making more money and selling that to us as an improvement? That's really the question and I don't think we got a frank answer there.

At least one study seems to suggest those sensors when deployed in automatic emergency braking systems do have a measurable impact on collisions.[2]

Let's say the failure rate on the sensors was 1 in 100 (I'd be shocked if that many were defective). That means 99 other Teslas are using mutli-sensor systems and not driving with degraded capabilities. It's an asinine claim that doesn't pass basic logic tests. The only way they weren't a substantial improvement is if Tesla's measurements were conducted in only the absolute most ideal conditions for cameras and no other scenarios.

[1]: https://en.wikipedia.org/wiki/Adaptive_cruise_control#Histor...

[2]: https://www.forbes.com/advisor/car-insurance/vehicle-safety-...

More likely they had a fixed budget and optimized with that constraint, if they made a rigorous decision at all. But this is guesswork.

I'm not speculating about how Tesla made the decision, just commenting on Karpathy's answer. His answer is correct even if it isn't true, i.e. even if it isn't what Tesla actually did.

There are plenty of well-known analogs, like the mythical man-month. We all know that throwing more x at a problem is routinely counterproductive, even without cost as a constraint.

> It seemed like all the "full cost" negatives Andrej mentioned were related to Tesla's ability to execute, and not what would actually produce better results.

This is objectively wrong, and it's the only substantive part of the discussion. The rest is fantasizing about things nobody actually knows ("It's media training!") and imputing questionable motives to someone who hasn't done anything to deserve that ("He only cares about Tesla's bottom line!").

I believe his point was to provide a new perspective on the problem, not to reduce the problem to a single reason. I highly doubt the only reason Tesla chose to use vision only in the short term was motivated by a single datapoint.

Even if it was the most important point... in this one person (on a large team’s) mind... it doesn’t necessarily mean it was the most important in the sum of the complex process it took to get to the decision.

So I don’t really see the value in taking it to the logical maximum because it’s not only illogical that they would be evaluating this one idea in isolation but even on its own they would still be balancing the optimal performance they got from x vs the optimized value they got from y, then compare it to the teams ability to work with both x+y(+z) at the same time.

For ex: You’d probably need 8 cameras pointing different directions vs one highly capable rapidly spinning LiDAR to even compete with it, so why even ask? These problems a) always have context and b) can't be so easily simplified and broken down.

Although you might make a good point that Tesla used this same poor logical-maximum reasoning to determine why not get rid of ALL sensors besides vision.

At least 6 are needed to get a 360 degree view around the car, which obviously is necessary. Think of the 8 cameras as a single better sensor. It's a question of having one very good sensor then or many to fuse.

We really should be focusing on what is the best solution and trying to solve price issues through existing techniques e.g. economies of scale, competition, miniaturisation. Instead they are trying to build whatever solution they can that fits in a pre-defined cost window.

Except this isn't a new phone or sneakers we are trying to take to market it's something that will directly impact people's lives.

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.

Why not have a thousand sensors if more is better?

This mindset is something I see a lot, that “best” means the technically optimal (or sometimes just personally most convenient) solution to the specific problem that they personally are working on. If they take a step back and look at the bigger picture, the technical merits are usually only a tiny part of the whole decision.

And all this only considers first order effects. If a 2x safer FSD feels more dangerous that normal driving and thus reduces FSD uptake for a decade, doesn't Tesla have a moral obligation not to release it to preserve the perception of safety of self driving technology?

I’m not saying this is definitely true, and at the moment we probably can’t verify it either. I’m just “steel manning” his case, as Lex loves to say.

I think you’re probably correct that the business aspect was a significant factor, but perhaps it wasn’t everything.

By getting rid of the extra sensors they eliminate a temporary crutch and focus resources on the simple solution.

Not a new concept by the way. Henry Ford was obsessed with simplifying and eliminating every part that wasn’t necessary on the model T for virtually all the same reasons.

Tesla is starting with something that doesn't work. No one has been able to achieve full autonomy yet, not even Waymo on its own turf, despite Waymo being well ahead of Tesla. I trust Tesla will be able to close the gap and be able to perform to the its current standards without radar and ultrasound, and it would be fine if the current standards weren't terrible in the first place. What I mean is that Tesla is currently at the awkward spot where it is good enough for cruise control, but not good enough to safely take a nap in the driver seat.

As for the "simple solution", you may know the saying "For every complex problem there is an answer that is clear, simple, and wrong". I think it applies here.

This move is purely about screwing passenger safety for cost and sales.

In a vacuum, how can cameras ever be better than cameras + other sensors?

I think this mischaracterizes Andrej's response. If anything he is referring to a wholistic view of the vehicle, which includes but doesn't entirely consist of Tesla. For example, 5-10 years down the road, when sensors start going bad, consumers will appreciate fewer things to go wrong with a vehicle--that is one of the advantages of electric over ICE after all.

If anything this is an acknowledgement that George Hotz was right in focusing on optical sensors with Comma.ai.

1) He's not touching on the software cost of integrating different sensor data into the same trained machine learning model; it is likely far simpler to just stick to stereoscopic vision data (the same thing the human genome decided!)

2) That said, it seems at least theoretically advantageous to have a sensory system that exceeds that which humans are limited to; things like LIDAR can work in complete darkness and potentially spot, for example, pedestrians crossing a dark road without any reflective clothing on, where a vision-based system would fail (perhaps add infrared sensation?)

Anyway, doesn't AEB (automatic emergency braking) have to be installed in every car, by law, in the US, around now? And wouldn't that be less reliable if done via vision?

There’s a lot more to perception while driving than just stereoscopic vision.

First, your stereoscopic “cameras” (eyes) are mounted in free-rotating sockets, which are themselves mounted in a rotating and swiveling base (your head/neck). Your eyes can do rapid single-point autofocus better than any existing camera. They also have built in glare mitigations —- squinting, sunglasses, and sun visors. This system is way more advanced than fixed cameras. Yes, even an array of fixed cameras with a 360 degrees field of view.

Then you have your sense of touch, your hearing, and your equilibrio sense. You feel motion in the car. You feel vibrations in the pedals. You hear road noise, other cars, sirens, and the engine (not much in EVs). You smell weird smells and know when you’re driving with your e-brake on or when there’s a skunk nearby. There’s a lot getting fused with the vision to make it all happen, and I think you’d be surprised how “broken” your driving capabilities would be if you took one of these “background” senses out of the equation.

My anecdote: I drive a manual transmission car. A few months back, I woke up with no hearing in my right ear. Spooked, I drove to urgent care. I could not drive well at all —- I was holding low gears for way too long. I learned that I use hearing almost exclusively to know when to shift. If you had asked me beforehand, I probably would have said that I’m visually monitoring the tachometer to know when to shift. Not the case. Also, I had a TERRIBLE sense of my surroundings. As I drive, I’m definitely building a model of the environment around me based on road noise, sound from other cars, sirens, and the like. Without hearing in just one ear, I felt very disconnected and unsafe. Living in California where lanesplitting is legal, I had several motorcycles catch me completely off guard. I had my hearing restored at urgent care and everything went back to normal immediately on the drive home.

I think Andrej and Tesla massively overestimate vision’s sole ability to solve the problem. Humans are fusing lots of sensation to drive well.

Yeah and till we had reliable and powerful artificial lighting, it was highly unsafe to journey in low visibility/ darkness. We used to finish journeys when darkness fell.

Animals that do require precise movement in low visibility (bats, dolphins) conditions often evolved ultrasound solutions.

So should we license Tesla vehicles to only operate when visibility and weather forecast is good and not drive in the dark at all?

https://www.science.org/content/article/human-eye-can-detect...

and also i dont understand your assertion that it was some kind of cynical maneuver to re-frame the question. he could have also said "yes, more sensors are always better but you can add an arbitrary number of sensors and so we had to decide where to draw the line. the cameras we use are capable of meeting our goal of full self driving that is significantly safer than a human driver. and this also streamlines the production and software which has a material impact on our ability to actually produce the cars which is of course necessary to meet the goal of making self driving cars. bloat could actually kill tesla."

this is logically the same thing that he said in the interview, so whats cynical about it? how is it underhanded?

also is there some intrinsic limitation of the dynamic range of cameras? people are talking about problems with dynamic range being intrinsic to cameras but im pretty sure that cameras and especially camera suites that do not have more problems with dynamic range than a human eye are possible to make and probably already on the market.

I think it's possible that professional movie cameras (with the appropriate lenses) may have higher dynamic range than human vision. Good luck getting those cheaper than a lidar.

a) Saying "I wonder when language models will do this" is a total Lex thing to say. That's what he's into.

b) Lex is almost always a softball interviewer, though one with interestingly deep knowledge. He interviews experts, and he errs on the side of respect. If you're looking for hardball, don't listen to Lex, it's not what he does. He almost never calls out bullshit, and he especially doesn't call out evasion.

Now, it's still possible that it was a panic-deployed evasion on Andrej's part, and whether or not that was his mentality, I agree that he gained nothing by it, and did not in fact reframe the question at all.

lex kisses elon musks ass, last time i checked hes on musks side in the lidar debate and also lex has a record of listening patiently no matter what and i have never known him to check people or "call out their bullshit." lastly, what andrej did wasnt a bullshit move. re-framing a question has never been known as something people do only when they are being deceptive and it is very common in intellectually honest answers/explanations in my experience.

im still shocked because usually i see what other people see. but people calling this a dig/bullshit came out of left field for me. i hope i dont miss stuff like that more than i realize...

edit: i read your comment again and i didnt read it right. he panics, gives himself some more room, lex acknowledges this but in a prickly way. it makes sense but im still upset because when i watch it all i see is lex making a joke. i guess i have severe autism.

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 agree with this assessment. However:

> Telsa can't support additional sensors without incurring a prohibitive amount of additional risk to Tesla. Risk to passengers doesn't appear to be a consideration.

This is a stupidifying take. Of course when you work in a line of business producing gadgets that, as an unintended side-effect, kill a lot of people (napkin math suggests above 2 milli-kills per car in the US), you will need to pick a point at which you say further fatality reduction is no longer justified given the economic cost of achieving it. Even if you are a pure altruist (if you go out of business, less safe cars will replace yours). Conversely, even if you are the embodiment of capitalist evil, risks to passengers will absolutely affect your bottom line and if are rational you will take them into consideration. Any meaningful criticism needs to be about the trade-offs they make, not that they make them or are loath to explicitly say so on camera.

You're right — the sad truth is that corporations put costs on human lives every day. Where I think we disagree is that you believe they made the decision based primarily on costs. After watching this video, I believe they made the decision because they didn't think they could reliably implement and support a sensor fusion approach.

(BTW, I enjoyed "stupidifying"! I'm sorry I made people stupider.)

percieved, not real risks to customers. PR matters more than reality

Because of the cost of additional eyes. If Tesla is optimizing for cost against safety, that's sort of the point.

I don't believe that's totally the case. Andrej later makes a better argument regarding limited R&D bandwidth, noise and entropy. But the "I would almost reframe the question" evasion was disconcerting. It's a textbook media trained tactic for avoiding a question to which you have no good answer. That it was deployed here badly against a skilled interviewer such that it backfired is a valid observation.