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I blame this largely on Telsa essentially driving the industry like a heard of cattle. This is/was a great when it came to accelerating the development of elect

I blame this largely on Telsa essentially driving the industry like a heard of cattle. This is/was a great when it came to accelerating the development of electric cars, and I realise that the software development community really likes the "move fast and break stuff" mantra, but there are reasons why engineering fields with chartered engineers have processes like FMEA: people die if things fail. A friend who is an engineer at a premium car manufacturer working on sensors for self-driving is telling me that the internal policy inside the manufacturer is, that the only competitor that matters is Tesla, so they only compare themselves to Tesla. The same friend also believes that real self-driving is still many years out, even sensors that can deal with general weather conditions do not exist yet.

deleted 6y ago
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kolinko 6y ago
How did your friend arrive at the conclusion that we don’t have good enough sensors? IMHO AI to analyse the data is what’s missing - we have cameras that are just as good as human eyes, so we know for a fact that the current sensors are enough to drive in general weather conditions.
- KaiserPro 6y ago
  Cameras don't give you depth. You have two choices: o have a sensor that gives you depth directly (ie laser, radar, or lenticular array) o try and infer depth from stereo cameras Lidars are expensive and power hungry, Radar is cheap and mature, but doesn't give you anywhere near as much resoultion lenitucular only has a tiny range. There are other time of flight sensors, but they are either not production ready, expensive or both. What Tesla have chosen to do is kinda try and merge radar and monocular object detection to give a higher frame rate depth estimation of objects. However its expensive to develop, unreliable, and terrible in corner cases (ie, if sees an unknown object, it can't place its depth.) Now humans can do this, because we've had years of training, Tesla can't. Tesla's fancy cruise control is dangerous. Its auto drive stuff its trying to develop is even more dangerous. Instead of finding the sensors and processing spec needed to drive safely, they are trying to use the sensors and GPU they already have. Its not going to work and is unsafe.
  - saalweachter 6y ago
    > Now humans can do this, because we've had years of training... Humans can mostly do this. Like 90% of UFO sightings are people seeing the moon and getting confused and thinking it is a nearby object following them.
  - ricardobayes 6y ago
    There are surprisingly sophisticated (and precise) machine learning algorithms for mono-camera depth estimation. It's not a problem anymore. Furthermore, most companies use various focal length lenses. The real problem lies in sensor fusion. Which sensor's input do you believe most, especially if one or more is occluded/dirty.
    - amelius 6y ago
      I have yet to see any ML algorithm that can reach 99.999% accuracy or better (we need better).
      - evdoks 6y ago
        Why do we even need such accuracy? Humans are extremely bad at estimating distance and nevertheless are quite ok drivers. ML needs to make sure that the car is not bumping at things or people, which is a very different task.
        amelius 6y ago
        Yes, so "can/cannot bump into" is a classification tasks which needs quite some accuracy. A human on the other hand can recognize another human with near perfect accuracy.
        Spivak 6y ago
        Because the failure modes are different. Humans with basically 100% accuracy both correctly recognize depth and put it into the right bucket of near/far but are woefully bad at estimating the numeric distance. So you’re right that you don’t need to do better than extremely coarse estimates like close, a few car lengths, in the distance, etc.. If computer vision fails at all to recognize depth (semi truck with clouds painted on it isn’t recognized) or misclassifies whether something is near or far that’s much more dangerous.
- myself248 6y ago
  A camera is a light sensor, a camera is not an object sensor. Cameras as light sensors definitely exist. A camera feeding into phenomenally sophisticated AI trained on all sorts of weather conditions with windshield wiper smear and glare and everything else, which can turn those horrible images into a picture of what actually lies ahead, is an object sensor. Those aren't good enough yet.

deleted 6y ago

[deleted]

kolinko 6y ago

How did your friend arrive at the conclusion that we don’t have good enough sensors? IMHO AI to analyse the data is what’s missing - we have cameras that are just as good as human eyes, so we know for a fact that the current sensors are enough to drive in general weather conditions.

KaiserPro 6y ago
Cameras don't give you depth. You have two choices: o have a sensor that gives you depth directly (ie laser, radar, or lenticular array) o try and infer depth from stereo cameras Lidars are expensive and power hungry, Radar is cheap and mature, but doesn't give you anywhere near as much resoultion lenitucular only has a tiny range. There are other time of flight sensors, but they are either not production ready, expensive or both. What Tesla have chosen to do is kinda try and merge radar and monocular object detection to give a higher frame rate depth estimation of objects. However its expensive to develop, unreliable, and terrible in corner cases (ie, if sees an unknown object, it can't place its depth.) Now humans can do this, because we've had years of training, Tesla can't. Tesla's fancy cruise control is dangerous. Its auto drive stuff its trying to develop is even more dangerous. Instead of finding the sensors and processing spec needed to drive safely, they are trying to use the sensors and GPU they already have. Its not going to work and is unsafe.
- saalweachter 6y ago
  > Now humans can do this, because we've had years of training... Humans can mostly do this. Like 90% of UFO sightings are people seeing the moon and getting confused and thinking it is a nearby object following them.
- ricardobayes 6y ago
  There are surprisingly sophisticated (and precise) machine learning algorithms for mono-camera depth estimation. It's not a problem anymore. Furthermore, most companies use various focal length lenses. The real problem lies in sensor fusion. Which sensor's input do you believe most, especially if one or more is occluded/dirty.
  - amelius 6y ago
    I have yet to see any ML algorithm that can reach 99.999% accuracy or better (we need better).
    - evdoks 6y ago
      Why do we even need such accuracy? Humans are extremely bad at estimating distance and nevertheless are quite ok drivers. ML needs to make sure that the car is not bumping at things or people, which is a very different task.
      - amelius 6y ago
        Yes, so "can/cannot bump into" is a classification tasks which needs quite some accuracy. A human on the other hand can recognize another human with near perfect accuracy.
      - Spivak 6y ago
        Because the failure modes are different. Humans with basically 100% accuracy both correctly recognize depth and put it into the right bucket of near/far but are woefully bad at estimating the numeric distance. So you’re right that you don’t need to do better than extremely coarse estimates like close, a few car lengths, in the distance, etc.. If computer vision fails at all to recognize depth (semi truck with clouds painted on it isn’t recognized) or misclassifies whether something is near or far that’s much more dangerous.
myself248 6y ago
A camera is a light sensor, a camera is not an object sensor. Cameras as light sensors definitely exist. A camera feeding into phenomenally sophisticated AI trained on all sorts of weather conditions with windshield wiper smear and glare and everything else, which can turn those horrible images into a picture of what actually lies ahead, is an object sensor. Those aren't good enough yet.