Yeah, but Google's vision + lidar tech also doesn't seem any better at solving it either. They have been working on this problem the longest and they aren't even confident enough to produce a product with it. Google is probably the leader in AI and AI research. They are also the leader in data and mapping. They have billions of cash to play with. Yet it seem like they haven't gotten any closer at solving this problem as well.
They are just going about it better but not trying to selling it.
Any reason why everyone seems to be stuck on this problem?
Waymo and Cruise routinely have driverless cars on city streets. In California, all collisions, however minor, have to be reported, and DMV posts them on their web site.[1] Most are very minor. Here's a more serious one from last month:
"A Cruise autonomous vehicle ("Cruise AV") operating in driverless autonomous mode, was traveling eastbound on Geary Boulevard toward the intersection with Spruce Street. As it approached the intersection, the Cruise AV entered the left hand turn lane, turned the left
turn signal on, and initiated a left turn on a green light onto Spruce Street. At the same time, a Toyota Prius traveling westbound in the
rightmost bus and turn lane of Geary Boulevard approached the intersection in the right turn lane. The Toyota Prius was traveling
approximately 40 mph in a 25 mph speed zone. The Cruise AV came to a stop before fully completing its turn onto Spruce Street due to the
oncoming Toyota Prius, and the Toyota Prius entered the intersection traveling straight from the turn lane instead of turning. Shortly
thereafter, the Toyota Prius made contact with the rear passenger side of the Cruise AV. The impact caused damage to the right rear door,
panel, and wheel of the Cruise AV. Police and Emergency Medical Services were called to the scene, and a police report was filed. The
Cruise AV was towed from the scene. Occupants of both vehicles received medical treatment for allegedly minor injuries."
Now, this shows the strengths and weaknesses of the system. The Cruise vehicle was making a left turn from Geary onto Spruce. Eastbound Geary at this point has a dedicated left turn lane cut out of a grass median, two through lanes, a right turn bus/taxi lane, and a bus stop lane. It detected cross traffic that shouldn't have been in that lane and was going too fast. So it stopped, and was hit.
It did not take evasive action, which might have worked. Or it might have made the situation worse. By not doing so, it did the legally correct thing. The other driver will be blamed for this. But it may not have done the thing most likely to avoid an accident. This is the real version of the trolley problem.
I think the way I would say it is, after a few tens to hundreds of thousands of such events, we'll be able to estimate the parameters of the human preference functions associated with a non-fatal trolley problem (the real trolley problem is irrelevant to the car ML folks, they are not aiming to maximize global utility) and that will help guide our confidence in rolling out more.
For all the woe and gloom in the news reporting, Google (and Cruise)'s rollouts have been more or less what I expected: no enormous accidents that were clearly caused by a computer, but instead, a small number of small accidents usually due to the human driver of another vehicle doing something wrong. That seems to lead towards greater acceptance of self-driving cars and confidence that they are roughly as good as an attentive newbie.
The next big situation, I think, will be some really large-scale pileup with massive damages and deaths, and a press cycle where the self-driving car gets blamed. But the self-driving car collected a forensic quality audit log, which of course will aid the police in determining which human caused the accident.
I've been reading those DMV reports for years, and there are clear patterns which repeat. One is where an autonomous vehicle started to enter an intersection with poor sight lines to the cross street. Sensing cross traffic, it stopped, and was then rear-ended by a human-driven vehicle that was following too closely. Waymo has that happen twice at the same intersection in Mountain View. There's a tree in the median strip there which blocks the view from their roof sensor until the vehicle starts to enter the intersection. So the Waymo system advances cautiously until it has good sensor coverage, then accelerates or stops as required.
Humans tend not to do that, and, as a result, some fraction of the time they get T-boned.
AI should absolutely mimic the behavior of real (good) drivers.
Although that's not what you're describing here, another problem for AI could result from it knowing more than an average driver; for example, if a high-mounted LIDAR were able to see around corners and let the car decide it's "safe" to do a turn that no human would attempt for lack of visibility, that could cause problems.
(Also, it's surprising that an autonomous car doesn't detect that another car is following it too closely, and slows down appropriately in anticipation. How is this not taken into account.)
An autonomous vehicle has to protect its occupants at the expense of everything else (or, at the very least, appear to do so in a convincing manner), because otherwise no one will step inside.
(At the very least, if a machine is going to sacrifice me or my family to save a third party, I need to know what hierarchy it is following, and how it was decided and by whom.)
But what this incident seems to illustrate is that it's difficult for a self-driving car to share the road with human drivers, and behave like a human -- meaning, allowing human drivers anticipate what it will do.
I drive a motorcycle and a bike in Paris; the reason I'm still alive is because after so many years of this I can tell what all the other cars will do at all times, before they know it themselves.
But an autonomous vehicle that would behave so differently from a human driver as to be unpredictable, would be terrifying.
Totally agree; beginner and truly dangerous drivers share a quality: unpredictability. Even the most aggressive, fast drivers can easily be accounted for when defensive driving. Unpredictable drivers are how collisions happen when speed and weather are not factors.
Even the simple automatic emergency braking features in my model 3 result in some dangerously unpredictable behaviour at times. I have them set as off and insensitive as possible and they still do some awful stuff from time to time. I was on a road trip in northern Ontario this week passing a service vehicle moving very slowly along the shoulder on the right. I was doing 105 km/hr in the right lane of a 3 lane road: 2 lanes in my direction and 1 opposing. The 2nd lane switches directions every 5-10 km to serve as a passing zone. Posted speed limit is 80 km/hr, but prevailing speeds on this road are 90-110 and you’d never be ticketed for anything under 110 in this region. There was a truck gaining on me coming up on the left, so I signalled left and partially moved over to give the service vehicle some room, but didn’t fully take the left lane to let the faster truck know I would let them through as they came past. Very common pattern on this type of road and circumstance. The AEB slammed on the brakes as I came level with the service vehicle despite the fact there was plenty of space to complete the pass. I wasn’t expecting my car to slow down let alone apply emergency braking force, so in my surprise I nearly collided with the service vehicle. I have no idea what the other drivers thought, but neither of them could have possibly been predicting or expecting me to slam on the brakes. I was really upset with the car since it took a highly dangerous action in an otherwise perfectly safe and common situation. And that was the automatic stuff that can’t be turned off; I don’t let autopilot drive ever because it is the worst type of driver: unpredictable. The choices it made in the extremely short time I tested it about lane placement, follow distance, defensive driving (and the complete lack thereof), and general behavioural clues provided to other drivers were genuinely terrifying.
> By not doing so, it did the legally correct thing. The other driver will be blamed for this. But it may not have done the thing most likely to avoid an accident. This is the real version of the trolley problem.
It performed the only sane solution to the problem - stopping. You can't possibly predict what the most likely thing is to avoid an accident, because there is a multitude of factors beyond just 2 cars moving towards each other. Are there pedestrians present? Other parked cars? Storefronts with customers inside? How close to the sidewalk? Trees? Construction/Debris?
Once we get AI working better than 99.9% of humans on roads with speed limits of up to 30 mph - we can expand it to faster roads and introduce advanced behaviors. But for now, stopping in uncertainty is the best available option.
> It performed the only sane solution to the problem - stopping. You can't possibly predict what the most likely thing is to avoid an accident, because there is a multitude of factors beyond just 2 cars moving towards each other. Are there pedestrians present? Other parked cars? Storefronts with customers inside? How close to the sidewalk? Trees? Construction/Debris?
Not only there are other factors, but you also need to predict what the other human driver will do. As stated in a sibling comment this is a huge part of safe driving.
> Once we get AI working better than 99.9% of humans on roads
But what you stated above require AGI, so what's the plan to get there? It's even more blurry than commercial fusion at that point.
Yes, this applies to autonomous vehicles being tested in the state. Tesla skirts this rule by reporting their system (including the supposedly ‘Full Self-Driving’ beta) as level 2 driver assistance features. That marks them out of scope for the regulation of reporting collisions (and crucially system disconnections).
Because self-driving has a bunch of tricky edge cases and most of them will kill people. Problems with hundreds of important edge cases cannot be solved by simply throwing more training data at the problem; that's how you solve AI problems in a "dumb" manner, and it works for lots of problems (like recognizing dogs in images) -- but not for self-driving.
To solve the self-driving problem we need "smart" A.I., which means we have to approach it with systematic engineering, and the solution will probably involve some combination of better sensors, introspectable neural nets, symbolic A.I., and logical A.I.
I remain convinced that "real" self driving (as in: go ahead and sleep in the backseat) will never happen without changes to road infrastructure and possibly some sort of segregation between robot-driven cars and people-driven cars.
Things like traffic signals that actively communicate their status to nearby robot cars (more than just a red lamp that can be occluded by weather, other vehicles, or mud on the camera lens). Or lane markings that are more than just reflective paint, but can be sensed via RF. Rules around temporary construction that dictate the manner of signage and cone placement that the robot cars can understand. The cones might have little transponders in them, I don't know.
But without a massive leap forward in AI capability, our current road system—optimized for human drivers over the past century—is not going to work.
If we can't make the cars just as smart as an alert and capable driver, then maybe we need to meet halfway and make the roads a littler "dumber" (simpler) to accommodate the robots.
> Things like traffic signals that actively communicate their status to nearby robot cars (more than just a red lamp that can be occluded by weather, other vehicles, or mud on the camera lens). Or lane markings that are more than just reflective paint, but can be sensed via RF. Rules around temporary construction that dictate the manner of signage and cone placement that the robot cars can understand. The cones might have little transponders in them, I don't know.
The problem you then face is that any of those could be forged / faked without some kind of way of securely validating the message in some way. You could cause absolute chaos by driving down the road broadcasting false messages. It's a little harder to hack and modify traffic light signals, for example. But we've also seen hackers screw up Tesla cars by sticking stuff on the back of their car to deliberate mislead it based on vision.
> You could cause absolute chaos by driving down the road broadcasting false messages
Even without self-driving cars, an "attacker" can go into a theater and yell "fire" and cause a stampede.
They can get a high-viz vest and clipboard, and stand in intersections directing cars to take detours they don't need and holding up traffic.
My point here is that society has a lot of trust baked in. We trust people don't just yell "fire" without reason. Just because it's FSD cars doesn't mean people will start broadcasting the equivalent of "fire" constantly. It's already easy to cause accidents.
Those attacks don't scale. They're also tied to a person or people who need to be physically present, making it possible to arrest them.
The consequences of potential attacks on centrally-orchestrated traffic are a lot more severe. Hack the control node, and you can stop traffic nation-wide. Or cause mass accidents that overwhelm first responders. And they can be executed by anyone, anywhere in the world, for a cost within range of many medium-size corporations (let alone nation states).
I won't comment on the challenges of the approach Tesla et al. are currently taking, but I don't think central control is the panacea commenters in this thread are making it out to be (and I'm personally glad this isn't the route we're pursuing).
An attacker can already do this, at scale. Whether it be overriding traffic lights to show green in all directions, or taking down critical air traffic control systems.
It’s like arguing that we can’t possibly build autonomous cars because then someone might turn it into an autonomous bomb.
Keep in mind that solving this is “worth” about 40,000 lives a year in the US - nearly $1 trillion in economic damages a year in life and property.
Bad things can always be done with good tools. As always, you provide layers of protection that make sense and in the end must rely on the underlying fabric of civilization to persevere.
Bad bad things.
I've had my Tesla on a highway either lose GPS precision and believe I was on an adjacent local road..
It immediately reduced speed from 60mph to 25mph .. aggressively.
This falls into a pattern of Tesla autopilot/NoA where it just doesn't seem to have much memory or foresight.
For example the car is driving itself on the highway, it knows it's been on the highway, for 20 minutes. I am not even in the exit lane, it knows what lane I am in. How could it think I am suddenly on the local road below the highway based solely on the GPS pin movement in the span of a second, without having moved to the exit lane and gone down the exit ramp?
For an example of lack of foresight - the car will happily speed towards an obvious semi-distant slowdown right until it needs to aggressively break from 60mph down to 30mph as it approaches following distance of the nearest car. I also find it can get really weird in stop&go traffic, not easing into speed, down to a stop very well as if it has only GO or STOP.
> For example the car is driving itself on the highway, it knows it's been on the highway, for 20 minutes. I am not even in the exit lane, it knows what lane I am in. How could it think I am suddenly on the local road below the highway based solely on the GPS pin movement in the span of a second, without having moved to the exit lane and gone down the exit ramp?
Oh wow. This happens often with a car-mounted GPS (or on a phone) and it's pretty annoying. Sometimes the GPS instructs you to do a U-turn at the next available fork in the road, and it takes a moment to understand what's going on.
But in a self-driving car it's terrifying! And absurd.
Who controls the signing keys and the whole signing process? What about key revocation if someone steals the key? Will a municipality in Texas really be willing to not be allowed to create a new stoplight without approval from the federal agency in charge of the keys? What’s to stop someone stealing a “real” stoplight from bumfuck nowhere and putting it in the middle of the 101 at rush hour? What about replay attacks? What about signal jamming? Etc. etc.
You raise some genuine possible concerns but generically I'd probably tend to just say, "laws will stop them" just like they already stop someone deliberately endangering lives by placing a real fake stop light in the middle of the 101 at rush hour.
The real concern would be whether someone can engineer a terrorist level mass scale attack but as long as it requires physical tampering that adds up to a tremendous amount of work. So if the signalling is largely burned into fixed infastructure it eliminates a lot of that or at least sets the bar high enough that its probably more work than various other types of attack that are likely to be just as impactful.
ADS Mode B (used everywhere for airplanes) already works this way, and there is no authentication or signing whatsoever. It's a single global broadcast frequency (1090MHz).
> The problem you then face is that any of those could be forged / faked without some kind of way of securely validating the message in some way. You could cause absolute chaos by driving down the road broadcasting false messages.
> I remain convinced that "real" self driving (as in: go ahead and sleep in the backseat) will never happen without changes to road infrastructure and possibly some sort of segregation between robot-driven cars and people-driven cars.
Yep, I talk to people working in traffic engineering, and their mindset is always building new road tech and road-side and cloud infra to support autonomous driving. They have no expectation of fully autonomous vehicle without road and infrastructure assistance.
And from historical perspective, the coming of automotive and the replacement of horse and other animal carts, are exactly facilitated by the road transformation; which has been the single largest scale infrastructure in human history.
It makes no sense that an even bigger transformation of the vehicle would require less drastic road transformation.
Yes. You bring up a great point: With some infrastructure improvements we could have "virtual" trains. Vehicles would talk to each other on the highway and organize into closely-spaced convoys. Only the lead vehicle would need an active human driver; the rest could follow at extremely close distances--drafting off each other--and would not need a human driver (or their human drivers could go off-duty). The point of using asphalt rather than rails is that it makes it easy to switch between individual car mode and automated virtual train mode.
This idea is not new and it mostly applies to freight convoys but I think it also has merit for ad hoc passenger car convoys on long highway trips.
> With some infrastructure improvements we could have "virtual" trains. Vehicles would talk to each other on the highway
I think the idea is to get rid of the roads and have the robo vehicles travel instead on tracks. That increases fuel efficiency and bypasses a lot of AI challenges.
Not only that. Overhead electrical solves a lot of the mining/environmental impact of batteries.
It’s the low-key case that Elon will be remembered poorly (like Robert Moses’ rapidly degrading legacy) for
1) Having the wrong vision for EVs (but successfully executing non it anyway)
2) Making space travel cheap (thereby increasing the amount of carbon energy dedicated to it) without really improving an average human’s quality of life
I wonder if all the "hype" from the Ubers and Googles about imminent self-driving is not actively harmful in that it is probably sucking out the oxygen from any perceived need to implement these things.
But for people, in the same way as for parcels, power, cable, fiber-optic, there's the last mile problem. Europe has almost solved it with high speed trains and efficient city transit. Then, it solves nothing for all those folks living in rural areas and having to drive f-150s.
> Then, it solves nothing for all those folks living in rural areas and having to drive f-150s.
Fortunately thats not a lot of people.
When electricity was being rolled out, Westinghouse and Edison didn’t bellyache about not being able to provide electricity to rural areas. They electrified all the cities.
Rural areas will just never be able to pay for modern infrastructure. And… thats ok, its not a lot of people.
I think the point was that in rural areas they'll still use cars or something, because it's way more efficient for the low density there. In cities, you would switch away from cars as much as possible. I don't think the commenter meant we'd "abandon the hicks" or something rude like that; the rural folks who farm and do other important jobs are a crucial part of our society for sure.
> Things like traffic signals that actively communicate their status
This is a thing in Europe, and even some US cities - my Audi has traffic sign recognition and when at a compatible intersection knows what the light is at (by radio, not by light), and how long until it changes (will show a countdown in seconds til the next light change).
> optimized for human drivers over the past century
Are our roads really? Most in cities over a certain age are just haphazard relics of times gone by, and don't get me started on "stroads" which are good for nobody
> If we can't make the cars just as smart as an alert and capable driver, then maybe we need to meet halfway and make the roads a littler "dumber" (simpler) to accommodate the robots.
This aspect of FSD has always fascinated me and I'm a little surprised it doesn't get more discussion. Meeting halfway. At what point could/would/should FSD influence the environment around it?
For example - a poorly painted road sign*. Tesla/Waymo could say "We cannot support L5 FSD on this road until you fix this sign." If it meant a step forward in autonomy, Tesla/Waymo could even offer to share the cost of that improvement!
There are a million reasons why implementation of that would be problematic. Costs and incentives would be all over the place. But I am more interested in the framing: The machines are the ones that need to adapt. Which is essentially hoping for continued hardware improvements or a spaghetti mess of if/else statements. ie "do this weird thing if you see this other weird thing in front of you". Can we get rid of the weird thing and avoid the engineering challenge altogether?
* Yes, this is an overly simple example. Some environment changes could be so large that they would require a full redesign of a city/buildings/traffic patterns. But surely there are classes of improvements where some are easier than others.
massive AI leap? but we can ask language models about how to drive through an intersection and it will list the steps correctly (and wildly incorrectly at other times)
what's missing is combining this kind of "human concept relations" model (language, rules, minimal reasoning, text encoded human preferences) with perception, and safety (which means that the model should know that if other cars are driving just fine in front then it's unlikely that the road is on fire, or that the low certainty crack in the road is okay if two other cars already went over it unimpeded, if the road marks and the signs are inconsistent, but other vehicles have formed a slow but consistent pattern of traffic then that's the local ruleset, and so on)
it's still a very hard problem. and the required amount of compute is still bonkers, the required amount of data and training is still absolutely huge, and the whole problem of safely disengaging, handling the asleep/drunk passengers (likely target audience after all)... are all hard problems too :)
You can get a self driving car ride and sleep in the back in Arizona, from Waymo. If you're on a special list, it sounds like you can do it in SF today with Cruise. Your claim is manifestly false.
`possibly some sort of segregation between robot-driven cars and people-driven cars.` just build out public transit at that point, what's the difference.
The pattern of zoning and development that the U.S. has used for the past 70 years assumes that nobody wants to walk or cycle anywhere and that they feel so strongly about it that they want cities to be built such that people will die if they attempt to do so. I grew up in a neighborhood where the elementary school was about half a mile away... on the other side of a five lane highway with no crosswalk and no sidewalk on the other side. Zero chance to assert your independence as a kid. Zero chance to live responsibly as an adult.
A part of the reason people wish they had FSD so badly is because they want to be rescued from this fundamental failure of NA-style urban planning that necessities driving, all the time, across both short and long distances.
This is changing though. Cities are getting bikes and bike lanes. They’re building dense developments close to transit. ebikes + protected bike lanes + TOD might be the solution.
I certainly hope so, and I've since moved to a place that is much more friendly for cycling; however if you go back to where I'm from (which for the purposes of this topic really could be one of any untold hundreds of counties in the U.S.), there are no signs of improvement or change. It's just the largest urban cores that seem to slowly be getting it. There is a breathtakingly large amount of suburban sprawl where the conversation about this topic is completely broken, and for the people willing to admit there even is a problem, they believe that the solution is one more lane, or one more stop sign, or just educating drivers a little bit better and nobody wants to talk about things like traffic calming, or upzoning, or cycle paths or anything else that makes anything other than car trips safe and desirable.
This is a bit of an oddity of US urban planning. The US has maybe 10 big to very big cities, where people can get around on foot, and then a host of what are called cities in the US but are essentially just collections of shopping centres, office parks, industrial estates, vaguely near each other.
It doesn't work like that in most countries; small cities and towns are navigable on foot and public transport.
Public transport often means buses, though, so self-driving AI is still very relevant there.
If you could replace double-decker buses that arrive every 15–30 minutes with self-driving minibuses that arrive every 3–5 minutes, that would be great! (for everyone except the bus drivers who lose their jobs)
Typically in cities the biggest constraint on bus frequency is bus congestion at bus stops (and to a lesser extent congestion in bus lanes), not number of buses. To the point where banning cash on buses can meaningfully increase system throughput, as it decreases lag time at bus stops. This can be alleviated with planning where people have to transfer to get anywhere (moving buses out of chokepoints) but in practice people don't like that.
Actually, I suspect this makes self-driving a particularly _bad_ solution for city buses; getting into the bus stops takes some manoeuvring, particularly when there are other buses there.
One place that self-driving buses could be interesting (and indeed there are already a couple of systems like this) is on fully/near-fully segregated lines, where they don't have to deal with human-operated traffic. Another would be small towns, but you're looking at full magic level 5 at that point.
I think a lot of those problems are solvable in principle, although unfortunately it’s probably not practical and incremental enough to actually happen...
I’m envisaging a system of minibuses, either AI-driven or at least dynamically directed by a central control system. People would use a phone app to book journeys; the app would tell them where to get on and where to transfer, and the central control system would optimise the fleet to get everyone where they need to go.
With much smaller buses, and electronic tap-in rather than cash payments, stops should be fast enough that buses can just queue up in order at each stop, hopefully mitigating the parking difficulties you mentioned (modulo breakdowns, medical emergencies, etc). Likewise, if transfers are fast and easy, hopefully they’d be less objectionable to travellers.
Requiring a phone isn’t ideal as it limits accessibility and privacy. There could also be pre-printed tickets, with QR codes that you scan at the bus stop to see the route info.
Why minibuses and not just taxis? I suspect there’s a good balance to be made between efficient road usage (buses) and efficient routing for each traveller (cars).
I’m also envisaging that if this system were to take off, personal cars could be gradually removed from city centres! Again, that makes life easier for the AI vehicles.
This is all pie-in-the-sky stuff, I know; but I do feel like there are ways we could radically improve city transport mostly using existing roads, rather than building new rails or tunnels.
A social one where you use experience to make inferences about what is going to happen next. Who hasn't observed an aggressive driver coming up from behind, weaving around other cars and said to themselves something like "that guy is going to cut me off, better ease off the gas so there's a little extra room in front for when he does". That level of situational awareness isn't coded for is it?
Edit: I have a whole mental model for other drivers and different approaches for them. Someone driving like a grandma? Pass when available. Nervous/erratic/lost driver? Keep extra distance then pass as soon as possible. Aggressive driver? Relax, give some space and let them get ahead. And so on. I get that stereotyping is bad but ignoring the subtle signals other drivers give off seems like it would be myopic. An AI that doesn't anticipate what others will do on the road will always be reactive rather than proactive.
Most self-driving vehicles aren't just coded; they incorporate some form of ML as well. Categorizing patterns of behaviors is well within the reach of ML algorithms, but my understanding is we have far more basic problems to solve first (Tesla's seems to struggle with object tracking over time, which would be a necessary first step to recognizing patterns).
Developing object tracking and a sense of object permanence would be a pretty big prerequisite.. And here I was thinking about the RL model needed to decide what to do in the presence of other agents.
How many people have been killed by Cruise or Waymo cars for the number of miles they've driven?
I maintain the position that if self-driving cars handle the most common situations as well as average human beings, there won't be a strong drive to make cars that drive significantly better than humans. Cruise and Waymo are collecting a limited version of that dataset right now. It's unclear how many deaths because some details are being kept under wraps.
I disagree with you. On the contrary, I think it can only be solved by throwing more data, but much more data that we have available now, to get first a smart AI that has common sense about the world (a world model). Then we can start trying to drive in it.
The counterexample to your point is that brand new 16-year-old human drivers seem to do a pretty good job of driving with very little training data. Yes, they do kill people and they're much worse drivers than 40-year-olds who have much more training data, but the fact that 16YOs kill as few people as they do with so little training data means some other mechanism than raw data quantity is important.
> Any reason why everyone seems to be stuck on this problem?
Because it's really, really difficult. A lot of AI-ish stuff pretty rapidly gets to the point where it _looks_ quite impressive, but struggles to make the jump to actual feasibility. Like, there were convincing demos of voice recognition in the mid-90s. You could buy software to transcribe voice on your home computer, and people did. And, now, well, it's better than in the mid-90s certainly, but you wouldn't trust it to write a transcript, not of anything important. Maybe in 2040 we'll have voice recognition that can produce a perfect transcript, and human transcription will be a quaint old-fashioned concept. But I wouldn't like to bet on it, honestly.
And voice recognition is arguably a far, far easier problem.
I'm old enough to remember all the breathless pronouncements in 2015 about how self-driving cars would be everywhere within 5 years. I was skeptical then and I'm still skeptical that it will ever happen outside of some limited range of well known paths that have been pre-determined to be safe - ie we'll have (and we do have) some self driving cars but they'll essentially be on a closed course and will not leave the course. There are already some small bus lines like that - the bus just goes around a closed circuit picking up and dropping off people always taking the same route.
I'd argue that voice recognition is in a worse place now — It creates a FALSE sense that it can do a good transcription, when it is actually corrupted in the worst way.
I took part in a legal deposition where an "AI Transcription Software" was being used. When I received the transcript it had numerous errors, but they were all subtle. More common names were inserted in place of the name that I said, e.g., "Kennedy" instead of "Kemeny". "You have a [something]" was transcribed as "I have a [something]", completely reversing the meaning. And many more errors.
The common thread between the errors was that what was inserted into the transcript would have been the MOST EXPECTED word or phrase, instead of the ACTUAL MORE SURPRISING (surprising in an information-theory way) word or phrase. It's evident that on top of the phoneme recognition layer, this transcription software checked questionable items against tables/graphs of most likely words to occur near the other words it confidently identified in that context. Makes a transcription sound great, but it is WRONG.
The result was that the "AI Transcription" actively destroyed key information and hid that destruction under the guise of a smoothly edited transcription.
Although this surely was not the intent of the system's creators, I cannot think of a better way to make a more evil transcription system.
I also imagine it is a space where...you very quickly get to a product that seems magical...but you can see in the data the places where your magical product will regularly be unable to make clear decisions. Unlike a lot of traditional product development, that knowledge makes it very hard for you to release, because the liability and ethical problems are very real.
It's somewhat unfair because we used to simply collect less comprehensive data on performance and therefore know less about our corner cases - but you don't get to live in the future without dealing with the problems of the future.
> You could buy software to transcribe voice on your home computer, and people did. And, now, well, it's better than in the mid-90s certainly, but you wouldn't trust it to write a transcript, not of anything important.
Is this the fault of software or people generally frequently using bad sound equipment in poor and noisy conditions, such as talking on the phone while driving, on the street, poor connection, wind/rain, etc. ?
Personally, because English isn't my first language, I frequently "fail" to transcribe what is being said and have to ask the other person to repeat themselves. It seems like an AI voice transcriber in 2022 is going to work better than me.
Even in ideal recording conditions, machines are quite bad at transcribing human speech. Look at subtitles on live TV shows, typically produced these days by machine transcription; they're typically barely usable.
>Any reason why everyone seems to be stuck on this problem?
ML maximalism focused on the narrow problem of 'solving driving' while not recognizing that any task as complex as driving requires probably something closer to general intelligence, and theoretically the field has been impoverished in favor of "throw more graphics cards at everything".
Couldn't agree more. Especially when it comes to city driving, which would obviously be necessary for robotaxis, when AI zealots promise "it'll be here in a year or two", I always wondered "Have these people ever driven in the city?" I mean, to drive in a city, you basically:
1. Need to understand all standard signage (seems possible with AI).
2. Need to understand all "unstandard" signage (not sure how possible).
3. Need to understand the cop with the thick NY accent yelling at you saying "Can't you see there's been an accident and the road is covered with glass you dufus? Turn the F around."
I can certainly see AI solving the problem of driving in specially designed limited access highways (which could also support normal human drivers), and that alone would be a huge benefit, but I never saw how so many were willing to make the leap to "robotaxis that can drive you anywhere in the city."
6. Need to reasonably predict what that human that just made eye contact with you would likely do next, and how that's different from what he might do when he doesn’t make eye contact with you. And all of that differs if you're in NYC or SF or small town, Indiana
7. Need to constantly read other drivers' body language (or "car language"?) and infer what their intensions are. I can constantly tell what a driver is literally thinking, by picking up on their subtle motions. Drifting over slightly (they're looking to change lanes but haven't yet turned on their blinker), they're slow to respond to green lights, or acceleration of forward traffic (e.g. they're on the phone, texting, etc.) I even routinely look at the eyes of the driver in front of me by looking in their rear-view mirror (yes this entirely possible and actually helpful). Are they looking down at their phone in their lap?
8. Generally understand the true context of things you're seeing. E.g a 4x8 sheet of plywood in the road, a squashed piece of road kill, a large metal pipe, a stopped vehicle, a baby carriage, a dishwasher, a tumbling large slab of styrofoam (all things I've encountered in the last year).
9. Is there a stopped firetruck in front of me (I list this because of the ludicrous fact that a Tesla plowed full-speed into such an obstacle).
7. Need to understand that drunk person staggering along the roadside has been repeatedly slipping off the sidewalk and there's a non-zero chance they trip and fall right in front of you.
8. Need to understand that sometimes the stripes you see aren't the real stripes, you're driving near sunset and are seeing the sun reflected off of old stripes that were painted over with glossy black (wtf WSDOT).
7.5. Need to understand that the drink person staggering _inside_ the robotaxi has just thrown up in the backseat. After they are dropped off, the robotaxi cannot pick up any new customers.
This is a dance move known as the "tenderloin lurch". Well, not really, the Tenderloin Lurch is when a person staggers up to a crosswalk, waits until they have a NO WALK sign and proceeds to cross, while ignoring all the drivers who about to run them over.
I've been in situations like this. In SF. I grew up here.
What you describe is the classic west coast non-confrontationalism. You're probably annoying enough that it's simply not worth it to counter anything you say because you just dive into a petty and self-aggrandizing argumentative mode.
Brash and confused conservatives think everyone agrees with them, but in reality people don't want to get caught up in absolute bullshit by joining any kind of interaction with them. This is a common pattern by now. You would do well to recognize it.
Even then, even if you can solve every case involving actual roads with perfect markings and intact signs and functioning signals, I’ve found myself just this week:
- driving across an unmarked grassy mound to park a car at a store in their designated area.
- paying a fee with coins to enter and exit a toll road.
- stopping to move around roadworks based solely on hand signals from one of the workers.
These aren’t even scratching the surface in terms of edge cases that could be encountered regularly.
People point at statements like "it just needs to drive better than the average driver" and "well lots of idiots drive".
On the flip side, lots of intelligent people with other skills do not have the confidence & comfort to drive outside of their towns local roads, or at all.
There are problem sets for ML like quant trading or phone album image tagging where you just need to be right 51% of the time to make money / be novel & useful.
We've seen plenty of examples showing ML is not appropriate for life&death problems like being the primary reviewer of diagnostic imaging for cancer screening.
The bar for ML to drive a car is much much higher, and I'm not even sure we have the right sensor suites feeding into the models, not to mention the compute capacity to operate at low enough latency.
Driving is a social problem, not a technical one. It's functionally the same as walking down a crowded sidewalk. The car is just a tool, just an extension of our bodies.
We can't build a robot which can walk down a sidewalk without running into people either. The sensor tech and mapping fidelity are red herrings. People drive well because only people are good at predicting human behavior.
This. I'm always amazed by a skilled taxi driver maneuvering their car at a busy street in Tokyo with bustling pedestrians and bikes, some of them are old and slow and others ignoring traffic lights, just passing them by centimeters. It's like as if the car is a part of their body and they're threading though a crowd. They have to anticipate everyone else's move. The cars really look like people here and others treat them as ones.
Sort of. Your wording actually assumes down to its core that driving is inherently social, when in fact I think it may be only provisionally social. Driving currently “is social” in a few senses, But of course the important (and obvious) one is that it currently involves accounting for human-operated vehicles.
Alternatively, an autonomous vehicle operator in a homogenous network full of other autonomous operators has capabilities and characteristics that greatly simplify failure modes. Maybe even majority autonomous, partially heterogeneous? You can literally slow or stop the whole show to deal with a catastrophic event. It’s still “social” but probably much reduced from the scenario where you’ve got the full scope of human expressivity behind the wheel.
The REAL problem is how do we take our roads to the crossover point where those simplified network features become accessible.
> “This assumes our roads are used exclusively by autonomous vehicles”
I not only assume it, I say it out loud: “[fully autonomous, …] maybe even majority homogenous, partly heterogenous?”
> “When our roads are not used exclusively by motor vehicles to begin with.”
I’m not following what you’re saying here in the context of the earlier clause. If you mean not used exclusively by autonomous vehicles, yes, and that’s why I’m pointing out the provisional aspect.
You know, I could be snide, and say, “We’ve already invented that — it’s called a train.” :D
But I’ve thought the same, too. If every car on the road is robot-controlled then it changes the problem. Modulo failures, discrete algorithms should behave predictably towards each other, like the unix API philosophy.
It seems hard to get there, though. Even today it’s a PITA to maintain API boundaries in simple libraries, never mind make sure that the new Tesla v12.4 Full Self Driving For Real This Time doesn’t trigger edge cases in Volvo v7.7a Actually Real Self-Driving We Promise.
Can we make software that allows cars to behave as predictably as rail cars, but without the rails? Maybe, but I expect only on limited-access freeways. I’m sure these robot-driven cars will remain incompatible with common road uses cases like pedestrians, cyclists, and children chasing balls.
I paid ~$10 for two rides after signing up as a regular ole user in Mesa AZ. It was great, the first ride was a bit nerve wracking, but the second felt very nice.
I certainly wouldn't argue with you that it isn't ready for prime time and wide distribution, but it is interesting to see their progress in San Francisco, a much different driving problem.
If it takes them 10 years to get to prod in Mesa, two (maybe three?) in SF, maybe they start shrinking that a lot in metros without winters. ¯\_(ツ)_/¯
Waymo has superior performance based on their historical statistics. It makes sense, since their lidar sensors capture more of the environment, and directly in 3D. Their AI also seems better QA’ed.
The Tesla AI Day[0] surprised me as it showed they only had a simple architecture for a very long time, simply feeding barely processed camera pixels to a DNN and hoping for the best with little more than supervised learning off human feeds. Their big claim to glory was that they rearchitectured it to produce a 2D map of the environment… which I thought they had years ago, and is still a far cry from the 3D modeling that is needed.
After all, sure, we humans only take two video feeds as input… But we can appreciate from it the position, intent, and trajectory of a wealth of elements around us, with reasonable probability estimates for multiple possibilities, sometimes pertaining to things that are invisible, such as kids crossing from nowhere when near a school.
Cruise also seems to have better tech; they had a barely-watched 2h30 description of their systems[1] which shows they do create a richer environment, evaluate the routing of many objects, and train their systems on a very realistic simulation, not just supervised training, which means it can learn from very low-probability events. They have a whole segment on including the probability that unseen cars may travel from perpendicular roads; Tesla’s creeping hit-or-miss are well-documented on Youtube.
> After all, sure, we humans only take two video feeds as input…
No, we use both our perception and proprioception when we drive or walk for that matter. We have two accute visual sensors mounted in a housing with six degrees of freedom and a sensor feedback mechanism.
We also have fairly sensitive motion and momentum sensors all throughout our bodies. Additionally we have audio sensors feeding into our positional model.
All this data is fed into an advanced computing system that can trivially separate out sensor noise from useful signal. The computing system controls the vehicle, navigates, and even performs low priority background tasks like wondering if the front door of the house was locked.
We have dozens of integrated sensor inputs. It's just silly to assume we only use our eyes when driving. They're certainly an important sensor for driving but definitely not the only one.
All the vision-only maximalists also ignore how insufficient Tesla cameras are compared to your human eyes in terms of resolution, dynamic range and refresh rates.
If you ever review your Tesla Dashcam footage you'll see that you can rarely even make out a license plate. The cameras are not even HD, let alone UHD.
Further the refresh rate is a paltry 36fps.
Drive the car at high noon or night and you realize how poor the dynamic range is with highlights and/or shadows get lost.
> Yeah, but I am sure Tesla's software can do the same.
They absolutely cannot. They won't even try, they require a driver to be there to be ready to take over with no notice. Their software also makes so many basic mistakes that even what they're allowing it to do is dangerously reckless.
There's been a lot of progress despite AVs not meeting intitial hyped predictions. Waymo and Cruise are operating driverless robotaxis in SF. We're probably a couple years from many major cities having them.
We often can figure out how to take a product that works 90% and bring it to 99%, and then to 99.9%. The engineering challenges involved in each nine are often vastly more than the percentages indicate, but they're conceivable. With AI we have absolutely no idea how much effort might be required to get to that next level of reliability. We hope that bigger models, or better AI technology might get us there, but there's also a chance that they won't.
Probably because it's really, really, really hard to solve the thousands of edge cases that occur in real-world driving situations. I don't think FSD happens until government gets behind it and starts putting infrastructure behind it. If we start building roads (and cars) to be highly visible to AI one way or another, it all becomes much easier.
>If we start building roads (and cars) to be highly visible to AI one way or another, it all becomes much easier.
Or we could build 1-dimensional roads, which would make the AI's jobs much easier. Like, we could put down two parallel piece of metal, which vehicles could "hook" onto somehow...
> Any reason why everyone seems to be stuck on this problem?
Because they're all trying visual- or line-of-sight methods only, I call this the "robo-human" fallacy in ML: trying to automate the processes that humans undergo so that you eventually have a drop-in replacement for a human. But that is a myopic and unimaginative approach because you could be re-assessing the system itself and eliminating inefficiencies that lead to poor performance.
In the autonomous vehicles space, there is massive potential for self-organizing swarm algorithms to control pelotons of cars, rather than individual cars with no intrinsic sense of the general flow of traffic. You wouldn't need a top-down "commander" style architecture, it could be designed so that cars only talk to their immediate neighbors and emergent patterns keep traffic flowing smooth and fast.
I have always been skeptical of the attempts to reduce the amount of information about the road that a car receives. (Moving from stereoscopic to monocular vision to save the cost of one camera seems just stupid.) But people who dream of "smart cities" really seem to see little more than The Jetsons in their mind, and it limits the scope of research to our detriment.
I’d like to hear an actual response from the people downvoting this comment. It’s an interesting perspective, and while I can imagine legit criticisims (SPOF, privacy concerns, coordination with vehicles outside the system, etc) actually articulating them would yield a more productive discussion than downvoting without comment.
How is AI supposed to confidently distinguish a real stop sign from someone/something holding up a picture of a stop sign?
Yes, this is a weird edge case, but I think it gets at the core issue being that it takes way more sophistication to release this tech into the wild then ppl would like to admit.
Has Zoox even gotten the permits to do fleet-scale driverless testing? Last I heard, they were still limited to 2 vehicles on specific streets in foster city. Doesn't seem very solved.
Google/Alphabet is stuck and will make advances in specific territories but will never get there without a fundamental change in approach. Their approach relies on very detailed mapping/modeling of specific terrain, so they can make a usable case sooner, but outside of the map/model territory, they're literally lost. And maps/models change constantly and rapidly.
Tesla is taking a fundamentally more broad and deep approach - working with the fundamental fact that a pair of visual sensors and a compute engine (eyes & brain) can successfully figure out driving in strange areas in real time, ergo, it should be possible without a map/model or lidar. Once they get it solved, it will be solved once and for all. Bigger gamble, bigger payoff. Equipping the car with dozens of eyes is the easy part. The question is whether enough compute power can be brought to bear on solving the recognition problems, and the edge cases. They have obvious issues with failing to recognize large objects like trucks in unexpected orientations, left turns etc. Using millions of miles of live human driver data as a training set is great, except that the average driver is really bad, so it's entirely polluted with bad examples, ESPECIALLY around the edge cases that get people killed. There, examples from professionally trained drivers, who really understand the physics and limits of the car, adhesion, traffic dynamics, etc, are what you want to train on, but that isn't what they have. It is also possible that even if the set of training data would actually be sufficient, the big question will kill them - perhaps the solution requires orders of magnitude more compute power to approach human performance, and they just don't have the hardware to simulate human compute power. So, have they just hit the limits of what their compute power can do?
I think Tesla's approach is fundamentally the way to go, as it is a general solution, compared to everyone else's limited map/model approach.
But both may require either or both a more specifically programmed higher-level behaviors, and/or something much closer to AGI than exists, something that has actual understanding of the machine-learned objects and relationships, which does not yet exist (if one is known, pleas correct me - I'd love to know about it).
They are just going about it better but not trying to selling it.
Any reason why everyone seems to be stuck on this problem?