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Tesla “can gather 1 billion miles of data per year”

Discussion in 'Autonomous Vehicles' started by strangecosmos, Sep 11, 2018.

  1. strangecosmos

    strangecosmos Non-Member

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    That quote comes from Worm Capital:

    I sent them a message asking if Tesla elaborated on how much data is actually uploaded, and what kind of data is uploaded (e.g. camera vs. GPS).

    If Tesla is going to be uploading 1 billion miles’ worth of driving videos per year, that is truly momentous from a computer vision perspective.
     
  2. Pale_Rider

    Pale_Rider Member

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    They surely mean other types of data. Cellular fees would bankrupt them uploading they much data. Lol
     
  3. strangecosmos

    strangecosmos Non-Member

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    I think they are uploading using customers’ home wifi.
     
  4. strangecosmos

    strangecosmos Non-Member

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    How many employees would Tesla need to manually annotate 1 billion miles of video per year?

    -1 billion miles per year

    -0.0088888 miles travelled on average per second of video (32 mph)

    -112.501 billion seconds of video

    -5 seconds on average to annotate one second of video

    -One employee works for 1,645 work hours per year (35 hours per week for 47 weeks), or 98,700 minutes per year

    -112.501 billion * 5 seconds = 9.375 billion minutes

    -9.375 billion minutes / 98,700 minutes per year = 94,985 employees

    So, using these assumptions, Tesla would need 95,000 employees (triple its current workforce) to annotate 1 billion miles of video annually.

    If Tesla were to outsource the annotating to developing world workers earning $2/hour, it would cost $313 million per year for 156 million hours (9.375 billion minutes) of labour. Hmm. That is actually not that much money. Tesla could probably afford to spend several times that amount.

    Alternatively, Tesla could have much more efficient ways of annotating video, such that it takes less than 5 seconds on average to annotate it. For example, a labeller might watch automatically annotated video at 4x speed and only stop to manually annotate something when they spot an error. Depending on the software’s error rate and the time it takes to fix errors, that could bring the time down to less than 1 second of labour per second of video.

    At 0.5 seconds of labour per second of video, that would bring the number of labelling employees needed to around 9,500. At $15/hour, their salaries would cost $268 million per year. Again, an affordable amount.
     
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  5. Pale_Rider

    Pale_Rider Member

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    Another interesting tidbit from a recent Electrek tweet, Tesla’s are averaging almost 20 million miles per day. That’s over 7 billion a year, which makes the 1 billion per year if road data into a little bit of perspective.
     
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  6. CapnLoki

    CapnLoki Member

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    Something way off with your numbers - since there are 200,000 Teslas in the wild, you're saying it would take half a person to track one, even going a 5X real time.

    Lets go at it differently - A billion miles at 32 MPH is about 30,000,000 hours. If one employee can go a 5X real time, and works 2000 hours a year, that's 10,000 hours per employee. That means 3000 employees.

    My question is, Why have humans annotate all the footage. Why not just look for "incidents" (close encounters, swerves, emergency braking, etc) and back up from there? You can, for instance, look for spots along the road that had repeated incidents. Lots of scenarios that don't require humans to analyze.
     
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  7. strangecosmos

    strangecosmos Non-Member

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    In terms of just Hardware 2 cars: there are roughly around 200,000. Each one drives 32 miles per day on average, and 8 miles per day on Autopilot. So that’s 6.4 million miles per day total (an annual run rate of 2.3 billion), and 1.6 million miles per day on Autopilot (run rate of 584 million).

    The number of HW2 cars should be updated quarterly because Model 3 deliveries are still ramping and of course that is going to make huge difference to the annual run rates. You can also toy around with spreadsheets and extrapolate into the future.
     
  8. strangecosmos

    strangecosmos Non-Member

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    I was actually assuming 0.2x real time. So 31.25 million hours of footage would create 156.25 million hours of work divided amongst employees who work 1,645 hours per year. That’s 95,000 employees.

    But of course if you assume they can go 2x real time, or 3x, or 5x, or 10x, or 20x, then the amount of work hours and employees needed is commensurately less.

    I would bet they pay close attention to any incidents or noteworthy events like that.

    But they also have to get the computer vision neural network(s) up to the level where they can go 200 million miles without making an error that would cause a fatal accident. So I bet there is a lot of labelling of cars, cyclists, pedestrians, barriers, sidewalks, trees, etc. trying to get that error rate down to superhuman levels.
     
  9. im.thatoneguy

    im.thatoneguy Member

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    You don't have to annotate 1 billion miles to be useful. You just have to annotate disengagements.

    For instance I disengaged about 6 times over 250 miles. So that has already reduced the human intervention by a factor of about 50x. Next, the cameras usually are correct, it's the path finding logic that's off. As an example: I would disengage to steer toward the center of the lane if it was drifting toward the outer edge with a truck. You wouldn't have to annotate "Lane" "Truck" "Sign" etc in that situation you might just use the arrow key on the keyboard to shift the optimal lane position to the other side. Then with enough examples it'll learn to give vehicles extra room when you overtake them if there is nothing on the other side.

    I wish for instance that while driving we could adjust "trim". We could drive in "Autopilot training" mode. In this mode Tesla could allow small amounts of force to nudge the car path. This could automatically capture a billion miles of extra training data with 0 human hours. Then the neural net could integrate the average of a billion hours of 'trim' data which would be nothing more than a -1.0 + 1.0 float value to cheat the lane position one way or the other.

    Similarly whenever the driver adjusts the speed limit, I turn down TACC when approaching a corner so that it can properly navigate at the correct speed. That's free data again for Tesla. "When did the user deliberately reduce speed below the speed limit?" It might notice that when the windshield wipers are at setting "4" average speed is LIMIT-5mph.

    Supervised learning is essential for teaching machine learning. But driving behavior can be unsupervised to a large extent since the data set is so massive. Waymo has a few dozen cars on the road. Tesla has a few hundred thousand. Once you refine the vision algorithms you can just upload metadata. First person shooters are a good example of this. You don't upload 16 player's 1080p rendered views to the central server, you just stream a few KB of metadata per second. An object's x,y position and category could be fit into 16 bits each (48 bits total). Even with 200 'interesting' objects in view (pretty high for average I would wager) that's a grand total of 9.6kb per frame. Assume 10 hz = 96kb/second * 60 seconds * 60 minutes = 34MB/hr * 30 days/month = 1GB/ month in data. That's a fleet wide dataset of only 300TB/year. That's still a sufficiently large interesting dataset for machine learning... while simultaneously being well within industry standards. That's less than one Backblaze storage pod and would require zero human intervention to collect.
     
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  10. verygreen

    verygreen Curious member

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    #10 verygreen, Sep 17, 2018 at 1:05 PM
    Last edited: Sep 17, 2018 at 1:18 PM
    That's a BIG assumption you have right there, don't you think?

    Important difference here is that in FPS the game is 100% sure about what it sees, other than that you are right, you can get by just on the objects if you accept this data is sometimes garbage (both ways - objects that are fake and real objects not represented). I suspect you can never have 100% vision in the foreseeable future. Even humans are fallible.
    Additionally you need more than 48 bits. you need things like speed, like how deep the objects are and such. And not all objects are created the same. A traffic signal object for example has extra states to a "vehicle" object.

    Edit: And I want to add that there's a way to relatively cheaply increase accuracy a great deal here - just include the actual camera picture every once in a while.
     
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