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It terrifies me to think of relying on image recognition software to correctly determine an upcoming crossing road (with cars zipping across) so it can properly

It terrifies me to think of relying on image recognition software to correctly determine an upcoming crossing road (with cars zipping across) so it can properly slow down, rather than get broadsided at full speed. Or a number of other life-and-death situations (which are common during driving). It just seems fragile (what if the 'vision' is somehow impaired, f.ex. blinded by sunlight, or the road markings are wrong or obscured) when the failure mode is so catastrophic. I suppose once statistics start to prove that these cars are safer than human-driven ones, we can chalk it up to an irrational fear, but for now it seems crazy to me to put my life in the hands of an AI, when a mistake means that I die or kill someone, rather than play the wrong song on Spotify. I don't fully understand why more effort is not put into a hardware solution, where roads are simply marked up for self-driving vehicles, e.g. magnets lining the lanes or something like that. Of course a more expensive solution, but seems like it would make the vehicles themselves a whole lot simpler and safer. Begin with inner cities, where the area is limited and traffic is most complex.

willvarfar 9y ago
> It terrifies me to think of relying on image recognition software to correctly determine (... snip) Do you not drive using only your eyes? If you are not terrified of the sensors, then software? Turing's central belief was that the human brain was 'just' a computer. Regards doing things like embedding reflectors in roads and other ways to simplify lane holding, completely agree. But we can't forego the cameras etc that deal with situations that don't contain reflectors.
- PakG1 9y ago
  If the brain is just a computer, its machine learning algorithms for image recognition at a busy intersection are believably currently more accurate than any image processing software run on any computer that Tesla puts in its cars.
  - kaeluka 9y ago
    There are ways in which current machine learning is better than your brain -- tracking many objects at the same time is one of them. Sure, it's worse in many other ways.
    - lttlrck 9y ago
      The ability to track multiple objects doesn't necessarily mean it's any good at integrating a solution. My camera can track multiple faces and smiles - that doesn't mean it can take better photos.
- TeMPOraL 9y ago
  The thing is, human "machine vision" has graceful failure modes. You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy. The same cannot be said about current ML demonstrations - because they operate on just vision. Humans continuously reconcile visual input with their model of the world and with other inputs, to the point of overriding visual data if needed. I suppose you could make ML avoid sharp changes in recognition, but I don't trust the current neural-network models to do so reliably.
  - aidos 9y ago
    I'm not sure the teslas are going to be trained to see elephants either!
    - asdfgadsfgasfdg 9y ago
      They will need to be if they are to be able to recognise all the potential real world phenomena they might encounter!
    - joeblau 9y ago
      You don't necessarily have to train it to see elephants, but Neural Networks fail in pretty interesting ways. This that are very obvious to you and I have the ability to confuse a CNN[1]. [1] - http://www.evolvingai.org/fooling http://www.evolvingai.org/fooling
      - ReverseCold 9y ago
        Those aren't too bad (out of context of the Tesla article), I can see how the ai might misinterpret those images. The bagel one, for example, is pretty on point.
        TeMPOraL 9y ago
        Those examples show that ML algorithms "see" things much differently from us. It also shows they're very vulnerable to nonsense data. How can I be reasonably sure that some random play of shadows and lights, that would never fool a human, will cause a failure mode in a neural network in a vision-only self-driving car?
- Cybiote 9y ago
  We don't drive with just our eyes. We fuse vision, sound, touch and proprioception. We self-locate easily. Most important though, is we do sophisticated visual, theory of mind and physics based inference. If you take the example of Chess, humans and machines play very different styles. Computers replace predictive understanding with excellent memory and fast search. Similarly for cars, we want to replace the lack of a powerful predictive model with some compensatory mechanism, such as sophisticated sensors. The problem is that, even with faster reaction times, better sensors and fuller awareness, these cars' compensatory abilities against a lack of powerful predictive models are still far from sufficient. At this point in time, a car with more sensors and failsafes to augment the human against collision is safer than a self-driving car that depends on a lack of lapses of attention. That depends on proper behavior in the customer. Even dividing tasks, with the human controlling the steering (but allowing the car to takeover when highly certain of danger) and the car controlling acceleration would be safer. This set up acknowledges the human propensity to mind wander when attention is not engaged. Until self-driving cars no longer require human rescue in difficult situations, one can't rightly say that self-driving software are better drivers than humans.
- eddyg 9y ago
  Is the "computer vision" on Tesla good enough to ignore this false "lane marker" and use the actual yellow lane marker, not the "fake" one caused by the sun/shadow/pavement interaction? http://i.imgur.com/fk3OvEW.jpg http://i.imgur.com/fk3OvEW.jpg This situation generated constant lane departure warnings for the driver.
  - rayiner 9y ago
    Here in the southeast, tons of roads have leftover sets of lane markings from when lanes were shifted over. Or no lane markings because of a lag between relaxing and marking lanes.
  - arcticfox 9y ago
    Not sure but that's pretty confusing to me too tbh without close inspection. The car in front seems to be treating it as a fog line too.
  - Matthias247 9y ago
    I can't speak for Tesla. But I have a newest generation VW with lane assist. And lots of road construction sites in my area, where the normal white lanes markers are "overridden" with some yellow lane markers, which are narrower and often also take different pathes. Once I'm inside a yellow lane and drive straight it halfway works and the lane assist keeps that lane. However on the first position where the lanes depart (e.g. normal white lane goes straight, but yellow lane turns left to change over to other side of the road) the car has not really an idea what to do and mostly follows the wrong lane --> Without manual intervention a crash in these conditions would be guaranteed.
- DennisP 9y ago
  The human visual cortex is far more powerful than the computers we can put in cars.
- intended 9y ago
  I really want to see one of these put on the road in India and see how it works. That's the real litmus test for me.
- takeda 9y ago
  We are far away from hard AI (computer actually can think) some people start to believe we might not be able to reach it. What we currently succeeding is developing soft AI (solving specific problems) the problem is that such AI might do fine when things are clearly labeled, but might not know what to do in unexpected situations. With car stopping might often be correct, but in other situations actually not stopping is best etc. Another thing is that your eyes can last you 100+ years, technology is not that durable and breaks more often, the objectives in cameras get dirty etc. The cameras typically aren't as good as human eye, for example there are reports where Tesla wanted to jam into a truck in front only because its back in the camera couldn't be differentiated from the sky. Yes, relying on cameras only is perfectly fine when you play PS4 game, but when life is on the line you want extra safety checks. Kind of like cars have brakes on every wheel and then also a handbrake. How every car has minimum of 3 stop lights (how often you see that one or more lights are bad in other cars?) Your belief that we have hard AI (and this is kind of indirectly created by companies like Tesla, Google, Amazon, IBM etc) gives you conclusion that just two cameras should be enough in reality we did not made much progress in that area and instead just concentrated on specialized AI that is capable solving specialized problems, that kind of approach absolutely works better with more sensors.
riskneural 9y ago
When did all algorithms become AI?
- daveguy 9y ago
  Whether an algorithm is AI essentially depends on whether the objective is 1) human level difficult and/or 2) heuristic. It should probably be both to avoid moving goal posts. That would make even chess algorithms still AI. Self driving cars and image recognition are both definitely still AI. "AI" does not typically mean strong or general AI.
FooBarWidget 9y ago
I agree that 'properly' marking roads for self driving vehicles is better. But this is a chicken and egg problem. There is no incentive to create such markings until self driving vehicles are common. And limiting self driving vehicles to properly marked roads will make them useless for most people, resulting in less funding for further development.
- tmzt 9y ago
  These would also apply to Lane Departure Warnings present on more and more cars each model year.
tigershark 9y ago
Seriously? Do you want to spend an incredibly huge amount of money to put magnets to delimit lanes all around the world?When there are so many roads without any clear painted lane marking, when the paint itself fades away in an handful of years and the magnets will last a couple of years at best ONLY if embedded in the asphalt, that means spending 100 times the amount of money above. And when in 2-3 years time we very likely will have self-driving cars far safer than humans. IMHO is pure nonsense. It will be a clusterfuck order of magnitudes worse than the British building thousands of km of canals when after 20-30 years the rails were dominating transportation. But at least they have the excuse that they didn't know what was coming, and 20-30 years is an order of magnitude longer than 2-3 years. Furthermore they spent an infinitesimal fraction of what you propose to spend.
- Reason077 9y ago
  "worse than the British building thousands of km of canals when after 20-30 years the rails were dominating transportation." I'm not sure that's a fair analogy. Without the canal network to cheaply transport goods and resources in bulk, it's hard to imagine how Britain could ever have industrialised to the point where building a railway network became possible in the first place! Besides, railways did not immediately obsolete the canals as you seem to suggest. Some canals were still being built and extended until the early 1900s (70+ years after the railway boom), and the last commercial freight services lasted until as late as 1970 - suggesting it was really motorways that "killed off" commercial canal traffic. One key point of difference is that (like road freight) pretty much anyone could operate a freight service using canals, where as the railways were in the hands of a few huge corporations. That meant a lot of niche, point-to-point freight was more viable to transport over water than rail.
Reason077 9y ago
"I don't fully understand why more effort is not put into a hardware solution, where roads are simply marked up for self-driving vehicles, e.g. magnets lining the lanes or something like that." How would "magnets" be any improvement over visual markings? Is there some sensor that can track magnets at greater distance, or with greater reliability than visual markings? Besides, how many millions of miles of road would need to be equipped with these magnets around the world? What would that cost? Would it be acceptable that autonomous cars could only drive on roads that have been properly equipped? It's worth remembering that self-driving AI does not solely rely on lane markings to navigate. It also can see the road edge, follow other vehicles, and cross-reference with a (learned/crowd-sourced) navigation model of the laneway. Additional sensor types, like LIDAR and radar, are often used in addition to cameras. You can see a lot of this at work, in a complex London traffic environment, in this demo: https://www.youtube.com/watch?v=cfRqNAhAe6c https://www.youtube.com/watch?v=cfRqNAhAe6c
- tmzt 9y ago
  A simple RF transmitter in new versions of the existing reflector discs would do it. It wouldn't need to do anything but "ping", the battery could last for years. You could even have a mechanism to disable it when the lines are repainted, though normally roads that use the existing version of these reflectors would have them removed when moving lines. The transmitter would have to be powerful enough to go through a small amount of snow in some states. The question is can we get the car companies to agree on a standard for this? Would it make sense to put it in the frequency range allocated for vehicle radar systems? What we get instead is an attempt to define the whole system de jure, including full V2V protocols and specifications.
  - Reason077 9y ago
    But what is the actual advantage of this RF system? Why is this any better than just using a camera and image processing? OK, RF would mean the lane geometry can be "seen" through all weather conditions (fog, snow). But so does precise-enough mapping/fleet learning. Neither would help in detecting traffic and transitory obstacles that might be hidden by that fog - you still need other sensors for that! A "safe" autonomous vehicle should perhaps simply slow down in adverse weather conditions just as a human driver would. Even without additional sensors, their ability to see through snow and fog is certainly no worse than a humans.
jacquesm 9y ago
They tried the magnet thing but it isn't flexible enough. For instance during roadworks you can't easily move the lanes, it only works when everything is perfect. There was a test track in NL with this technology a few years ago.
whazor 9y ago
The fear is irrational because even now you can always be hit by other drivers who are not as careful as you. In my home town, certain people are driving like crazy.. Ignoring traffic lights, going to fast, call or text during driving, etcetera. With autonomous driving, these people can game, work, drink etc instead. The main benefit of AI is that it drives more predictable and follows the traffic rules. Furthermore, cars themselves have become safer. If you look to traffic fatalities per country. You can observe that modern countries have less fatalities. Because of airbags, cripple zones, safer knocking of pedestrians.. And also traffic rules. To conclude, even though sensors might fail, they are better than human sensors.
ant6n 9y ago
Start with highways - they are grade separated, there are no pedestrians, no traffic lights, no intersections (besides merging), driving is often long and boring.
extrapickles 9y ago
It doesn't have to rely on vision. They probably use GPS+map for a low resolution location and trajectory planning. Vision should only be used for fine positioning and obstacle detection.

willvarfar 9y ago

> It terrifies me to think of relying on image recognition software to correctly determine (... snip) Do you not drive using only your eyes? If you are not terrified of the sensors, then software? Turing's central belief was that the human brain was 'just' a computer. Regards doing things like embedding reflectors in roads and other ways to simplify lane holding, completely agree. But we can't forego the cameras etc that deal with situations that don't contain reflectors.

PakG1 9y ago
If the brain is just a computer, its machine learning algorithms for image recognition at a busy intersection are believably currently more accurate than any image processing software run on any computer that Tesla puts in its cars.
- kaeluka 9y ago
  There are ways in which current machine learning is better than your brain -- tracking many objects at the same time is one of them. Sure, it's worse in many other ways.
  - lttlrck 9y ago
    The ability to track multiple objects doesn't necessarily mean it's any good at integrating a solution. My camera can track multiple faces and smiles - that doesn't mean it can take better photos.
TeMPOraL 9y ago
The thing is, human "machine vision" has graceful failure modes. You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy. The same cannot be said about current ML demonstrations - because they operate on just vision. Humans continuously reconcile visual input with their model of the world and with other inputs, to the point of overriding visual data if needed. I suppose you could make ML avoid sharp changes in recognition, but I don't trust the current neural-network models to do so reliably.
- aidos 9y ago
  I'm not sure the teslas are going to be trained to see elephants either!
  - asdfgadsfgasfdg 9y ago
    They will need to be if they are to be able to recognise all the potential real world phenomena they might encounter!
  - joeblau 9y ago
    You don't necessarily have to train it to see elephants, but Neural Networks fail in pretty interesting ways. This that are very obvious to you and I have the ability to confuse a CNN[1]. [1] - http://www.evolvingai.org/fooling http://www.evolvingai.org/fooling
    - ReverseCold 9y ago
      Those aren't too bad (out of context of the Tesla article), I can see how the ai might misinterpret those images. The bagel one, for example, is pretty on point.
      - TeMPOraL 9y ago
        Those examples show that ML algorithms "see" things much differently from us. It also shows they're very vulnerable to nonsense data. How can I be reasonably sure that some random play of shadows and lights, that would never fool a human, will cause a failure mode in a neural network in a vision-only self-driving car?
Cybiote 9y ago
We don't drive with just our eyes. We fuse vision, sound, touch and proprioception. We self-locate easily. Most important though, is we do sophisticated visual, theory of mind and physics based inference. If you take the example of Chess, humans and machines play very different styles. Computers replace predictive understanding with excellent memory and fast search. Similarly for cars, we want to replace the lack of a powerful predictive model with some compensatory mechanism, such as sophisticated sensors. The problem is that, even with faster reaction times, better sensors and fuller awareness, these cars' compensatory abilities against a lack of powerful predictive models are still far from sufficient. At this point in time, a car with more sensors and failsafes to augment the human against collision is safer than a self-driving car that depends on a lack of lapses of attention. That depends on proper behavior in the customer. Even dividing tasks, with the human controlling the steering (but allowing the car to takeover when highly certain of danger) and the car controlling acceleration would be safer. This set up acknowledges the human propensity to mind wander when attention is not engaged. Until self-driving cars no longer require human rescue in difficult situations, one can't rightly say that self-driving software are better drivers than humans.
eddyg 9y ago
Is the "computer vision" on Tesla good enough to ignore this false "lane marker" and use the actual yellow lane marker, not the "fake" one caused by the sun/shadow/pavement interaction? http://i.imgur.com/fk3OvEW.jpg http://i.imgur.com/fk3OvEW.jpg This situation generated constant lane departure warnings for the driver.
- rayiner 9y ago
  Here in the southeast, tons of roads have leftover sets of lane markings from when lanes were shifted over. Or no lane markings because of a lag between relaxing and marking lanes.
- arcticfox 9y ago
  Not sure but that's pretty confusing to me too tbh without close inspection. The car in front seems to be treating it as a fog line too.
- Matthias247 9y ago
  I can't speak for Tesla. But I have a newest generation VW with lane assist. And lots of road construction sites in my area, where the normal white lanes markers are "overridden" with some yellow lane markers, which are narrower and often also take different pathes. Once I'm inside a yellow lane and drive straight it halfway works and the lane assist keeps that lane. However on the first position where the lanes depart (e.g. normal white lane goes straight, but yellow lane turns left to change over to other side of the road) the car has not really an idea what to do and mostly follows the wrong lane --> Without manual intervention a crash in these conditions would be guaranteed.
DennisP 9y ago
The human visual cortex is far more powerful than the computers we can put in cars.
intended 9y ago
I really want to see one of these put on the road in India and see how it works. That's the real litmus test for me.
takeda 9y ago
We are far away from hard AI (computer actually can think) some people start to believe we might not be able to reach it. What we currently succeeding is developing soft AI (solving specific problems) the problem is that such AI might do fine when things are clearly labeled, but might not know what to do in unexpected situations. With car stopping might often be correct, but in other situations actually not stopping is best etc. Another thing is that your eyes can last you 100+ years, technology is not that durable and breaks more often, the objectives in cameras get dirty etc. The cameras typically aren't as good as human eye, for example there are reports where Tesla wanted to jam into a truck in front only because its back in the camera couldn't be differentiated from the sky. Yes, relying on cameras only is perfectly fine when you play PS4 game, but when life is on the line you want extra safety checks. Kind of like cars have brakes on every wheel and then also a handbrake. How every car has minimum of 3 stop lights (how often you see that one or more lights are bad in other cars?) Your belief that we have hard AI (and this is kind of indirectly created by companies like Tesla, Google, Amazon, IBM etc) gives you conclusion that just two cameras should be enough in reality we did not made much progress in that area and instead just concentrated on specialized AI that is capable solving specialized problems, that kind of approach absolutely works better with more sensors.

riskneural 9y ago

When did all algorithms become AI?

daveguy 9y ago
Whether an algorithm is AI essentially depends on whether the objective is 1) human level difficult and/or 2) heuristic. It should probably be both to avoid moving goal posts. That would make even chess algorithms still AI. Self driving cars and image recognition are both definitely still AI. "AI" does not typically mean strong or general AI.

FooBarWidget 9y ago

I agree that 'properly' marking roads for self driving vehicles is better. But this is a chicken and egg problem. There is no incentive to create such markings until self driving vehicles are common. And limiting self driving vehicles to properly marked roads will make them useless for most people, resulting in less funding for further development.

tmzt 9y ago
These would also apply to Lane Departure Warnings present on more and more cars each model year.

tigershark 9y ago

Seriously? Do you want to spend an incredibly huge amount of money to put magnets to delimit lanes all around the world?When there are so many roads without any clear painted lane marking, when the paint itself fades away in an handful of years and the magnets will last a couple of years at best ONLY if embedded in the asphalt, that means spending 100 times the amount of money above. And when in 2-3 years time we very likely will have self-driving cars far safer than humans. IMHO is pure nonsense. It will be a clusterfuck order of magnitudes worse than the British building thousands of km of canals when after 20-30 years the rails were dominating transportation. But at least they have the excuse that they didn't know what was coming, and 20-30 years is an order of magnitude longer than 2-3 years. Furthermore they spent an infinitesimal fraction of what you propose to spend.

Reason077 9y ago
"worse than the British building thousands of km of canals when after 20-30 years the rails were dominating transportation." I'm not sure that's a fair analogy. Without the canal network to cheaply transport goods and resources in bulk, it's hard to imagine how Britain could ever have industrialised to the point where building a railway network became possible in the first place! Besides, railways did not immediately obsolete the canals as you seem to suggest. Some canals were still being built and extended until the early 1900s (70+ years after the railway boom), and the last commercial freight services lasted until as late as 1970 - suggesting it was really motorways that "killed off" commercial canal traffic. One key point of difference is that (like road freight) pretty much anyone could operate a freight service using canals, where as the railways were in the hands of a few huge corporations. That meant a lot of niche, point-to-point freight was more viable to transport over water than rail.

Reason077 9y ago

"I don't fully understand why more effort is not put into a hardware solution, where roads are simply marked up for self-driving vehicles, e.g. magnets lining the lanes or something like that." How would "magnets" be any improvement over visual markings? Is there some sensor that can track magnets at greater distance, or with greater reliability than visual markings? Besides, how many millions of miles of road would need to be equipped with these magnets around the world? What would that cost? Would it be acceptable that autonomous cars could only drive on roads that have been properly equipped? It's worth remembering that self-driving AI does not solely rely on lane markings to navigate. It also can see the road edge, follow other vehicles, and cross-reference with a (learned/crowd-sourced) navigation model of the laneway. Additional sensor types, like LIDAR and radar, are often used in addition to cameras. You can see a lot of this at work, in a complex London traffic environment, in this demo: https://www.youtube.com/watch?v=cfRqNAhAe6c https://www.youtube.com/watch?v=cfRqNAhAe6c

tmzt 9y ago
A simple RF transmitter in new versions of the existing reflector discs would do it. It wouldn't need to do anything but "ping", the battery could last for years. You could even have a mechanism to disable it when the lines are repainted, though normally roads that use the existing version of these reflectors would have them removed when moving lines. The transmitter would have to be powerful enough to go through a small amount of snow in some states. The question is can we get the car companies to agree on a standard for this? Would it make sense to put it in the frequency range allocated for vehicle radar systems? What we get instead is an attempt to define the whole system de jure, including full V2V protocols and specifications.
- Reason077 9y ago
  But what is the actual advantage of this RF system? Why is this any better than just using a camera and image processing? OK, RF would mean the lane geometry can be "seen" through all weather conditions (fog, snow). But so does precise-enough mapping/fleet learning. Neither would help in detecting traffic and transitory obstacles that might be hidden by that fog - you still need other sensors for that! A "safe" autonomous vehicle should perhaps simply slow down in adverse weather conditions just as a human driver would. Even without additional sensors, their ability to see through snow and fog is certainly no worse than a humans.

jacquesm 9y ago

They tried the magnet thing but it isn't flexible enough. For instance during roadworks you can't easily move the lanes, it only works when everything is perfect. There was a test track in NL with this technology a few years ago.

whazor 9y ago

The fear is irrational because even now you can always be hit by other drivers who are not as careful as you. In my home town, certain people are driving like crazy.. Ignoring traffic lights, going to fast, call or text during driving, etcetera. With autonomous driving, these people can game, work, drink etc instead. The main benefit of AI is that it drives more predictable and follows the traffic rules. Furthermore, cars themselves have become safer. If you look to traffic fatalities per country. You can observe that modern countries have less fatalities. Because of airbags, cripple zones, safer knocking of pedestrians.. And also traffic rules. To conclude, even though sensors might fail, they are better than human sensors.

ant6n 9y ago

Start with highways - they are grade separated, there are no pedestrians, no traffic lights, no intersections (besides merging), driving is often long and boring.

extrapickles 9y ago

It doesn't have to rely on vision. They probably use GPS+map for a low resolution location and trajectory planning. Vision should only be used for fine positioning and obstacle detection.