>So we will have overall a much smaller number of deaths caused by self driving cars
Why? This is what the self-driving cars industry insists on, but has nowhere near been proven (only BS stats, under ideal conditions, no rain, no snow, selected roads, etc -- and those as reported by the companies itself).
I can very well imagine a greater than average human driving AI. But I can also imagine being able to write it anytime soon not being a law of nature.
It might take decades or centuries to get out of some local maxima.
General AI research had also promised the moon once again in the 60s and 70s, and it all died with little to show of in the 80s. It was always "a few years down the line".
I'm not so certain that we're gonna get this good car AI anytime soon.
If self-driving cars 1. don't read texts whilst driving, 2. don't drink alcohol, 3. stick to the speed limit, 4. keep a 3-4s distance to the car in front, 5. don't drive whilst tired 6. don't jump stop signs / red lights it will solve a majority of crashes and deaths. [0]
The solutions to not killing people whilst driving aren't rocket science but too many humans seem to be incapable of respecting the rules.
But it doesn't work like that. You can't just say "If they don't do X Y or Z", because while they may not do X Y or Z, that doesn't mean they won't do A B or C that are equally bad or worse. Human and self-driving are two completely separate categories, you can't just assume that things one does well the other also does well, and so just subtract the negatives. You could easily flip your comment to go the other way: "If human drivers don't mistake trucks for clouds or take sharp 90o turns for no reasons then they're safer".
I do think that self-driving cars will be safer, but it's upon it's proponents to prove that.
As the sibling comment says, it does depend on self-driving cars matching human level performance. But with all AI/Neural Networks it is very possible to match human performance because most of the time you can throw more human-level performance data at it.
Each of the crashes that self-driving cars can be fixed and prevented from happening again. The list I gave are human flaws that will almost certainly never be fixed.
I further agree with you it's up to the proponents to prove that. It's a good thing to force a really high bar for self-driving cars. Then assuming the technology is maintained once AI passes the bar it should only ever get better.
> Each of the crashes that self-driving cars can be fixed and prevented from happening again. The list I gave are human flaws that will almost certainly never be fixed.
Not if you put neural networks / deep learning in the equation. This stuff is black boxes connected to black boxes, that work fine until they don't, and then nobody knows why they failed - because all you have is bunch of numbers with zero semantic information attached to them.
Neural Networks are only a small part of self driving car algorithms. The planning and sensor fusion etc. is usually not done with deep learning (for this reason). Only visual detection, because we have nothing else working better in this realm. But lidar, radar, sonar, what have you all work without any deep learning. The decision making on a high level is also without deep learning.
The only questionable parts will be where the vision system fails, and those are similar actually to human problems. Because human vision also often fails (sunlight on windshield, lack of attention, darkness, etc.)
> But with all AI/Neural Networks it is very possible to match human performance because most of the time you can throw more human-level performance data at it.
Are you in very vague words implying that AGI has been invented? AI might have matched humans in image recognition, but it is far away in general decision making.
And finally, I am tired of listening to "safer than a human". That should never be the comparison, but a human at the helm and an AI running in the background which will take over when the human does an obvious mistake -- you know, like a emergency braking system,
"Each of the crashes that self-driving cars can be fixed and prevented from happening again."
If those situations recur exactly as they happened the first time, sure they can be prevented from happening again.
That is, if a car approaches the exact same intersection as the exact same time of day, and a pedestrian that looks exactly like the pedestrian in this accident crosses the street in exactly the same way, with exactly the same other variables (like all the other pedestrians and cars around there that the sensors can see), the data could be enough the same that the algorithm will detect it at close enough to the original situation to avoid the accident this time.
But it's not at all clear how well their improvements will generalize to other situations which humans would consider to be "the same" (ie. when any pedestrian in any intersection crosses any street).
If self-driving cars are, at their best, roughly as capable as a human driver.
This is a big 'if'.
The solution to not killing people is a kind of rocket science. In fact, it's probably harder than rocket science[0]. It's predicated on a lot of things that are very, very, very hard. The fact is that humans, who are already pretty capable of most of these very very hard things, often choose to reduce their own capabilities.
If the best self-driving tech is no better than a drunk human, however, then we haven't gained much.
I really don't think it's a big 'if'. As long as there is human level performance data, neural networks can be trained to match that level of performance. So it's a matter of time. It is indeed very, very hard, but also solvable.
However, the process your describing, of collecting human-level performance data, requires the ability to gather all of the data relevant to the act of driving in a manner consumable by the algorithm in question. This is the simulation problem, and it's very, very, very hard (it's why genetic algorithms have traditionally not gotten much further than toy examples, in spite of being a cool idea). Perhaps it is the case that it is very important to have an accurate model of the intentions of other agents (e.g., pedestrians) in order to take preventative action rather than pure reaction. Perhaps it is very important to have a model of what time of day it is, or the neighborhood you're driving in. The likelihood that it is going to rain some time in the next hour. Whether the stock market closed up or down that day.
It also assumes that neural networks (or the more traditional systems used elsewhere) are sufficiently complex to model these behaviors accurately. Which we do not yet have an answer to yet.
So, when I say, 'a big if', I mean for the foreseeable future, barring some massive technological/biological breakthrough. That could be a very long time.
Well, one answer is either it will be positively demonstrated to be statistically safer, or the industry won't exist. So once you start talking about what the industry is going to look like, you can assume average safety higher than manual driving.
> This is what the self-driving cars industry insists on, but has nowhere near been proven
Because machines have orders of magnitude fewer failure modes than humans, but with greater efficiency. It's why so much human labour has been automated. There's little reason to think driving will be any different.
You can insist all you like that the existing evidence is under "ideal conditions", but a) that's how humans pass their driving tests too, and b) we've gone from self-driving vehicles being a gleam in someone's eye to actual self-driving vehicles on public roads in less than 10 years. Inclement weather won't take another 10 years.
It's like you're completely ignoring the clear evidence of rapid advancement just because you think it's a hard problem, while the experts actually building these systems expect fully automated transportation fleets within 15 years.
> It's why so much human labour has been automated. There's little reason to think driving will be any different.
Repetitive, blunt, manual labor now, and probably much basic legal/administrative/medical work in the near future. But we still pay migrant workers to harvest fruit, and I don't imagine a robot jockey winning a horse race anytime soon.
Driving a car under non-ideal conditions is incredibly complex, and relies upon human communication. For example: eye contact between a driver and pedestrian; one driver waving at another to go ahead; anticipating the behavior of an old lady in an Oldsmobile. Oh, the robots will be better drivers eventually, but it will be awhile. We humans currently manage about one death per hundred million miles; Uber made it all of two million. I expect we'll have level 5 self-driving cars about the same time we pass the Turing test.
> Harvesting fruit is far more complex than driving. It's a 3D search through a complex space.
Are you making a joke? "a 3D search [for a path that reaches the destination safely and legally] through complex space" is exactly how I would describe driving. (Also, driving is an online problem.)
Ever heard of an elevated highway, ramp, flying junction, bridge, or tunnel?
I mean, yeah the topology is not as complex as a pure unrestricted 3d space but it's also more complex than pure 2d space. It's a search through a space, and it's complex, I don't know if nitpicking about the topology adds a lot here?
That's still 2D space. A car simply can't move along the z axis, so the fact that the road itself moves in 3 dimensions is irrelevant.
Even navigational paths that consider all of the junctions, ramps, etc. are simply reduced to a weighted graph with no notion of any dimensions beyond forward and backwards.
Your comment is just random hopeful assertions though...
>It's why so much human labour has been automated.
But how much human labour that is as complicated as driving has been automated? As far as I can tell automation is very, very bad when it needs to interact with humans who may behave unexpectedly.
>b) we've gone from self-driving vehicles being a gleam in someone's eye to actual self-driving vehicles on public roads in less than 10 years. Inclement weather won't take another 10 years.
>It's like you're completely ignoring the clear evidence of rapid advancement just because you think it's a hard problem, while the experts actually building these systems expect fully automated transportation fleets within 15 years.
Actually plenty of experts within the field disagree with you.
“I tell adult audiences not to expect it in their lifetimes. And I say the same thing to students,” he says. “Merely dealing with lighting conditions, weather conditions, and traffic conditions is immensely complicated. The software requirements are extremely daunting. Nobody even has the ability to verify and validate the software. I estimate that the challenge of fully automated cars is 10 orders of magnitude more complicated than [fully automated] commercial aviation.”
Steve Shladover, transportation researcher at the University of California, Berkeley
With autonomous cars, you see these videos from Google and Uber showing a car driving around, but people have not taken it past 80 percent. It's one of those problems where it's easy to get to the first 80 percent, but it's incredibly difficult to solve the last 20 percent. If you have a good GPS, nicely marked roads like in California, and nice weather without snow or rain, it's actually not that hard. But guess what? To solve the real problem, for you or me to buy a car that can drive autonomously from point A to point B—it's not even close. There are fundamental problems that need to be solved.
Herman Herman, Director of the National Robotics Engineering Center @ CMU
Automated food production is very much simpler, because you're usually only producing one food item at large scale. That's the super easy stuff to automate.
Automated driving is more like a fully automated chef, that can create new dishes from what his clients tell him they like. Without the clients being able to properly express themselves. That's a lot more complicated than following a recipe.
Difficulty of automation goes roughly trains < planes << cars.
Automated trains are simple, but don't provide much value. Automating planes provided value because it's safer than just with human pilots. Automated cars are a different league of complexity.
> But how much human labour that is as complicated as driving has been automated?
Driving is not complicated at its core. Travel along vectors that intersect at well-defined angles. Stop to avoid obstacles whose vectors intersect with yours.
Sometimes those obstacles will intersect with your vector faster than you can stop, which is probably what happened to this woman. As long as the autonomous car was following the prescribed laws, then it's not at fault, and a human definitely would not have been able to stop either.
> Merely dealing with lighting conditions, weather conditions, and traffic conditions is immensely complicated. The software requirements are extremely daunting.
Which is why self-driving cars don't depend on visual light, and why prototypes are being tested in regions without inclement weather. Being on HN, I'm sure you're well familiar with the product development cycle: start with the easiest problem that does something useful, then generalize as needed.
> With autonomous cars, you see these videos from Google and Uber showing a car driving around, but people have not taken it past 80 percent. It's one of those problems where it's easy to get to the first 80 percent, but it's incredibly difficult to solve the last 20 percent. If you have a good GPS, nicely marked roads like in California, and nice weather without snow or rain, it's actually not that hard.
Right, so the experts agree with me that the problem the pilot projects are addressing is readily solvable, and that general deployment will take a number of years of further research, but isn't beyond our reach. This past year I've already read about sensors that can peer through ice and snow. 15 years is not at all out of the question.
Driving isn't just travel along a vector. Maybe trains, but not urban roads. urban roads are full of people and animals.
If a ball bounces in front of me, I slow down expecting a dog or a child running after it. No self driving car now, and in 30 years is going to be able to infer that.
Driving is essentially interacting with the environment, reading hand signals from people, understanding intent of pedestrians, bicycles and other drivers. No way any AI can do that now.
> Driving isn't just travel along a vector. Maybe trains, but not urban roads.
Trains travel along a straight line, not a vector in 2D space.
> If a ball bounces in front of me, I slow down expecting a dog or a child running after it. No self driving car now, and in 30 years is going to be able to infer that.
Incorrect. I don't know why you think humans are so special that they're the only system capable of inferring such correlations.
Why? This is what the self-driving cars industry insists on, but has nowhere near been proven (only BS stats, under ideal conditions, no rain, no snow, selected roads, etc -- and those as reported by the companies itself).
I can very well imagine a greater than average human driving AI. But I can also imagine being able to write it anytime soon not being a law of nature.
It might take decades or centuries to get out of some local maxima.
General AI research had also promised the moon once again in the 60s and 70s, and it all died with little to show of in the 80s. It was always "a few years down the line".
I'm not so certain that we're gonna get this good car AI anytime soon.