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I remember back in 2016 I was sitting on my cousin's deck for one of his kid's first birthday parties, and my uncle posed a question to the group of whether the kid in question would ever get a driver's license. Now, he has a habit of going out on certain limbs when arguing, but he seemed utterly convinced that fifteen years hence autonomous vehicles would be so ubiquitous as to obviate the need for any driver training among normal people. I argued against the idea, but only to the extent that the regulatory landscape wouldn't change that fast—I certainly thought the technology would be there, but I doubted that regulators and insurance companies would have the stomach to turn all operations over to computers. OF course, that was around the time where everyone was talking about AVs. A guy near me trying to win the Democratic nomination for state rep was basing his entire campaign on handling the disruption that would soon wreak havoc on the trucking industry. I saw Uber's AVs on an almost daily basis near my office in Pittsburgh. CGP Grey was making videos about how full autonomy would basically solve traffic congestion, at least as long as you don't give a fuck about pedestrians.
This summer, that kid will be halfway toward qualifying for a learner's permit, and autonomous vehicles seem further away now than they did when he was one. Less than two years after that party, a woman in Arizona was killed after being hit by an Uber self-driving car. From the evidence available, it didn't look to me like the accident was avoidable, and had it involved a standard car it would have made the local news for a couple days but probably wouldn't have even resulted in charges being filed. But since it was an AV the story went national, and the public's trust had eroded. It would be easy to blame this incident for the downfall of enthusiasm over EVs, but let's face it; something like this happening was only a matter of time, and the public response was entirely predictable. So the industry plugged along, and keeps plugging along, though fewer and fewer people seem to care. Uber's out, Ford's out, Volkswagen's out, GM is under investigation, Apple seems directionless and indifferent, and a recent Washington Post article claims that Tesla cut quite a few corners in its pursuit of offering its customers something that could be marketed as progress.
Hype for AVs started picking up in earnest among the tech horny around 2012. Three years later the buzz was mainstream. All throughout this period various industry leaders kept making bold predictions about truly autonomous products being only a few years away. Okay, maybe with some caveats, like only on the highway, or in geofenced areas, or whatever, but still, you'd at least be able to get something that had some degree of real autonomy. The enthusiasm seemed justified, though, since, practically overnight, self-driving cars went from something that you'd occasionally hear about in science magazines when some university was doing basic research to something that major tech and auto companies were sinking billions of dollars into. Around the same time, regular cars started getting features like adaptive cruise control and lane keep assist that seemed like self-driving under another name, and Tesla's autopilot feature seemed like a huge leap. With the normal acceleration of technology plus the loads of money that were being dumped into any number of competing companies, it was only a matter of time. Now, ten years and 100 billion dollars later, the only products that are available to an average consumer are a few unreliable ridesharing services in cities that don't have weather.
I'm bringing this up because there are a lot of parallels between AVs and GPT-4. This is a huge, disruptive technology that relies on AI, and, while it may have some critical flaws in its current implementation, technology is constantly improving, often exponentially, as processing power increases. And while I don't have access to GPT-4 myself, I'm sure it's as impressive as everyone claims it is. The trouble is, impressing people with no skin in the game is easy. Convincing people to rely on it is a whole different animal. Most people found AVs pretty impressive when they first came out. But being impressive doesn't cut it when you're looking to replace human drivers; you actually have to be better than human drivers, or at least as good as human drivers. And human drivers are pretty damn good. In 2021 there were around 5.2 million reportable accidents in nearly 3 trillion miles driven (in PA an accident is reportable if one of the cars is inoperable or there is injury or death, though other states may vary). This means that, in any given mile of driving, one's chances of getting into an accident more serious than a fender bender is .000181%. If you drive 15,000 miles a year, you'll get into an accident about once every 30 years. If Elon Musk or whoever announced that they had developed a system that avoided accidents 99.9% of the time, that would sound impressive. But it wouldn't be; at that rate, the average driver would be getting into about 15 crashes per year! Even if it were 99.99% of the time you'd still be getting into more than a crash a year, 3 in a 2 year period. Imagine what your insurance rates would be like if you got into a crash a year.
And that doesn't even take into account all the miscellaneous bullshit that AVs do that doesn't cause accidents but nonetheless makes them untenable. They have trouble with unprotected left turns (aka most left turns), and they'll take circuitous routes to avoid them. They don't like construction, even minor construction like a lane being blocked off with cones. They get confused when, say, a landscaper has mulch bags hanging into the street a little bit. Or when driving down a narrow street with cars parked on both sides. And when this happens they just stop and call home. The people who use these ride sharing services are then forced to wait while a tech shows up to deal with the problem, traffic being disrupted in the meantime. And I won't even mention inclement weather. Making something look impressive during early testing is easy, but convincing someone to rely on it when safety, or money, or anything else that actually matters is at stake is a much harder sell, as the accuracy has to be pretty damn near close to 100% before anyone will actually trust it. And if AVs are any indication, it's really hard to get to 100%. Which is why I wouldn't be surprised if AI right now is at about the same stage AVs were in 2016. Impressive, but far from ready for prime time. Everyone keeps saying that the next iteration is going to be a game changer, and everyone is increasingly impressed, but not impressed enough to trust their business to it. And eventually it gets to the point where research is so expensive and the returns are so little that no one in their right mind would invest in it, and smaller firms go bust while larger ones scale back considerably, or at least try to direct their AI research towards applications where it might actually be used commercially. Then we're all sitting here in 2030 asking ourselves what happened to the AI revolution that seemed right around the corner. I could be wrong, but if that's the case, then hey, we should at least have some operable self-driving cars.
Partially on topic: https://zoox.com/
That's right: Amazon wants a taste of self driving vehicles. And to their credit I think this is the best approach. It's a way to dodge the fentanyl zombies who infest local busses and trains, not a personal car that you own.
And I've seen Google's truly self driving vehicles in person repeatedly. I'm surely not an expert, but Tesla's self driving might work a lot better if they slapped some LIDAR on it. Google and Amazon went big on LIDAR. Machine vision really doesn't get the job done as of today.
We might actually get real self driving in some limited areas. It's not going to go to 100% self driving, but in limited urban areas it will be more than zero in the near future.
Your "doesn't get the job done" link doesn't seem to go anywhere... I had to clip out everything past the "mediaplayer" portion of the URL to get to the video, where a tesla slams into a test dummy. But it doesn't take much work to find counterexamples, and this wouldn't be the first time someone fabricated a safety hazard for attention.
I don't think LIDAR is as big of a differentiator as tech press or popular analysis makes it out to be. It's very expensive (though getting cheaper), pretty frail (though getting more durable), and suffers from a lot of the same issues as machine vision (bad in bad weather, only tells you that landmarks have moved rather than telling you anything you can do with this info, false positive object identification). And this is trite, but remains a valid objection: human vision is sufficient to drive a car, so why do we need an additional, complex, fragile sensor operating on human-imperceptible bandwidth to supplement cameras operating in the same bandwidth as human eyes?
Tesla's ideological stance on machine vision seems to be: if camera-based machine vision is insufficient to tackle the problem, we should improve camera-based machine vision until it can tackle the problem. This is probably the right long-term call. If they figure out how to get the kind of performance expected from a self-driving system out of camera-based machine vision, not only have they instantly shaved a thousand bucks of specialty hardware off their BOM, arguably they've developed something far more valuable that can be slapped on all variety of autonomous machines and robotics. If the fundamental limitations are in the camera, they can use their demand in automotive as leverage to encourage major camera sensor manufacturers to innovate on areas where they currently struggle (high dynamic range, ruggedness, volume manufacturability). Meanwhile, there's a whole bunch of non-Tesla people working independently on many of the hard problems in the software side of machine vision; some of the required innovations in software don't necessarily need to come from Tesla. And if it does need to come from Tesla, they've put enough cameras and vehicle computing out in the wild by now that they could plausibly collect a massive corpus of training data and fine-tune it better than pretty much any other company outside of China.
Google, meanwhile, had years of headstart on Tesla, a few hundred billion dollars of computers, at least one lab (possibly several) at the forefront of machine vision research, extremely deep pockets to buy out tens of billions of dollars of competitors and collaborators, limited vulnerability to competitive pressure or failure in their core revenue stream, and a side business mapping the Earth compelling them to create a super-accurate landmark database for unrelated business ventures. I think the reason Google's self-driving vehicles work better than Tesla's is because Google held themselves to ludicrously high standards, half of which were for reasons unrelated to self-driving, and the likes of which are probably unattainable for more than a handful of tech megacorps. That they use LIDAR is immaterial - they've been using it since well before the system costs made commercial sense.
As for the rest of Tesla's competitors... when BigAutoCorp presents their risk management case to the government body authorizing the sale and usage of self-driving technology, it sounds a lot more convincing to say "cost is no obstacle to safety" as you strap a few thousand bucks of LIDAR to every machine and spend another few dozen engineering salaries every year on LIDAR R&D. A decade of pushing costs down has brought LIDAR to within an order of magnitude of the required threshold for consumer acceptance. I'll note that comparatively, camera costs were never an obstacle to Tesla's target pricing or market penetration. Solving problems with better hardware is fun, but solving problems with better software is scalable.
That's not to say Tesla's software is better though. I can't tell if Tesla's standards are lower than their competitors, or if their market penetration is large enough that they have a longer tail of publicized self-driving disasters to draw from, or if there's a fundamental category of objects their current cameras or software can't properly detect. Speaking from experience, I've seen autopilot get very confused by early-terminating lane markers, gaps in double yellow for left turns, etc. I think their software just kinda sucks. It's probably tough to identify the performance differences in good software with no LIDAR and bad software with LIDAR; comparatively much easier to identify bad software with no LIDAR. And really easy to blame the lack of LIDAR when you're the only people on Earth foregoing it.
The problem with the Tesla stance is that cameras (affordable ones, anyways) are still way behind human eyes -- it's not just dynamic range, the resolution/FOV tradeoff is extremely bad.
This guy estimates you would need ~576MP streaming at 30FPS with a FOV of 120 degrees to get close (actual FOV is more than that; depends how many cameras you want to have I guess). Such a system would be way more expensive than a LIDAR unit, safe to say -- especially if you expect to catch up with the 14 stop DR, which might not even be possible with current sensors.
Not sure what Tesla is using for resolution, but the extra acuity is surely not wasted in terms of picking out faraway objects and even figuring out roadlines -- this eats into the theoretical reaction-time advantage of AVs substantially.
This is not quite right. Eyes have a huge overall FOV, but the actual resolution of vision is a function of proximity to foveation angle, and there's only maybe a 5° cone of high-resolution visual acuity with the kind of detail being described. Just taking the proposed 120° cone and reducing it to 5° is more than a 99% reduction in equivalent megapixels required. And the falloff of visual acuity into peripheral vision is substantial. My napkin math with a second-order polynomial reduction in resolution as a function of horizontal viewing angle puts the actual requirements for megapixel-equivalent human-like visual "resolution" at maybe a tenth of the number derived by Clark. None of that is really helpful to understanding how to design a camera that beats the human eye at self-driving vision tasks though, because semiconductor process constraints make it extremely challenging to do anything other than homogenously spaced CCDs anyway.
On top of that, the "30FPS" discussion is mostly misguided, and I don't actually see that number anywhere in the text; I only see a suggestion that as the eye traverses the visual field, the traversal motion (Microsaccades? Deep FOV scan? No further clarity provided) fills in additional visual details. This sounds sort of like taking multiple rapid-fire images and post-processing them together into a higher-resolution version, something commercial cell phone cameras have done for a decade now. This part could also be an allusion to the brain backfilling off-focus visual details from memory. It's unclear what was meant.
This is already a solved problem, and has been for at least five years. Note that in five years, we've added 20dB dynamic range, 30dB scene dynamic range, bumped up the resolution by >6x (technically more like 4x at same framerate, but 60FPS was overkill anyway), and all that in a module cost that I can't explicitly disclose but I can guarantee you handily beats any LIDAR pricing outside of Wei Wang's Back Alley Shenzhen Specials. And it could still come down by a factor of 2 in the next few years, provided there's enough volume!
In any case, remember that the bet isn't beating the human eye at being a human eye, it's beating the human eye at being the cheap, ready-today vision apparatus for a vehicle. The whole exercise of comparing human eye performance to camera performance is, and has always been, an armchair philosopher debate. It turns out you don't need all the amazing features of the human visual system for the task of driving, this is sufficient but not necessary for a solution to the problem. You need a decent performance, volume-scalable, low-cost imaging apparatus strapped to a massive amount of decent performance, volume-scalable, low(ish)-cost post-processing hardware. It's a pretty safe bet that you can bring compute costs down over time, or increase your computational efficiency within the allocated budget over time. It's also a decent bet that the smartphone industry, with annual camera volumes in the hundreds of millions, is going to drive a lot of that camera manufacturing innovation you need, bringing the cost down to tens of dollars or better. Most of the image sensors are already integrating as much of the DSP on-die as possible, in a bid to free up the post-processing hardware to do more useful stuff, and that approach has a lot of room to grow in the wake of advanced packaging and multi-die assembly innovations in the last ten years. All the same major advances could eventually arrive for LIDAR, but it certainly didn't look that way in 2012, and even now in 2023 it still costs me a thousand bucks to kit out an automotive LIDAR because of all the highly specialized electromechanical structures and mounting hardware, money I could be using to buy a half-dozen high-quality camera modules per car...
As far as reaction time, real-time image classification fell to sub-frame processing time years ago, thanks in part to some crazy chonker GPUs available in the last few years. There's a dozen schemes for doing this on video, many in real-time. The real trouble now is chasing down the infinitely long tail of ways for any piece of the automotive vision sensing and processing pipeline to get confused, and weighing the software development and control loop time cost of straying from the computational happy path to deal with whatever they find.
This is also why I think Tesla's software just sucks. It's not the camera hardware that's the problem any more, and the camera hardware is still getting better. There's just no way not to suck when the competition is literally a trillion-dollar gigalith of the AI industry that optimized for avoiding bad PR and waited an extra four years to release a San Francisco-only taxi service. Maybe if Google was willing to stomach a hundred angry hit pieces every time a Waymo ran into a wall with the word "tunnel" spray-painted on it, we'd have three million Waymos worldwide to usher in a driverless future early. I doubt Amazon has any such inhibitions, so I guess we'll find out soon just how much LIDAR helps cover for bad software.
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