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Is it though? What Google has is certainly better than the publicly available lane and crash assist Tesla has, but Tesla has already shown demos of true autonom

Is it though? What Google has is certainly better than the publicly available lane and crash assist Tesla has, but Tesla has already shown demos of true autonomous driving, in fog and noting pedestrians, etc. It likely just needs a lot of training but cars are already on the road with hardware. Is Google ahead for a final product? Quite possibly, but they don't have a product and Tesla doesn't look far behind sortware wise, though it is hard to tell.

Fricken 10y ago
Krafcik mentions in his presentation that their cars are down to .2 disengages every 1000 miles (which would more aptly be described as 1 disengagement every 5000 miles), and their cars are actively seeking out challenging situations, it's not just easy/random driving. This is incredible. They are leagues ahead of the nearest competition. Krafcik also mentions they are making the lions share of their progress now using their simulation engine, where they can model every conceivable variation of any bizarre/difficult encounter they have on real roads. In sim they accumulated a billion miles in 2016.
- wstrange 10y ago
  A lot of folks have been saying that Tesla will win, because they will gather more data from real conditions. This never made sense to me. You certainly need enough data, but how you interpret and process that data is far more important.
  - datguacdoh 10y ago
    Also, how much of that data can you realistically send back to yourself? With Waymo they can pull the arrays from the cars nightly if they need to. Tesla, on the other hand, has to send the data back over a customers network connection which would be much more limiting. So Tesla might be getting tons of miles but if that's a billion not very detailed miles, it might not be as useful as a million incredibly detailed miles.
    - robszumski 10y ago
      I think Tesla pays for the network connection to all of its cars. This sends back remote telemetry and other self-driving data. It also serves up software updates and monitors the cars network for intrusions via a VPN.
      - datguacdoh 10y ago
        That network connection is using cellular, so at most it's an LTE connection but if I remember right, is actually just 3G. I can't believe they are pushing the kind of data people are talking about over that kind of connection.
  - adevine 10y ago
    It makes sense because virtually all machine-learning algorithms work better, and can learn faster, if you have more data. Think of it this way: if Tesla wants to test a particular algorithm for a particular driving situation, they can "play it back" over an enormous amount of real-world situations. They will have tons more potential edge cases with which they can validate their algorithms.
    - flylib 10y ago
      the data they have is not detailed enough though has been the criticism
    - renesd 10y ago
      This used to be true. Modern machine learning needs way less data. The classic example is taking images and then transforming them in hundreds of ways (scaling, rotation, skew, etc) for training. Big data is no where near as much a competitive advantage as it was three years ago. It seems not everyone outside the field has noticed that though.
      - adevine 10y ago
        I wonder if this would be true in the case of self-driving car algorithms, though (which I know nothing about). It always seemed like the hard part about self-driving cars was the 0.1% edge cases where something out of the ordinary could result in a catastrophe if not handled correctly. Image classification seems like it would be very different, most importantly that 99.9% "correct" would be a great achievement, but for self-driving cars a .1% failure rate would be completely unacceptable.
        elmar 10y ago
        We are 30 to 50 years away of a level 5 car.
      - eanzenberg 10y ago
        Lul thinking machine learning is only image classification
  - stuckagain 10y ago
    The popular idea that Tesla will just keep stuffing matrices down the throat of their training pipeline until a self-driving inference model emerges from the other end doesn't make a lot of sense.
    - Fricken 10y ago
      Neural nets can take you most of the way there, but pattern recognition alone will not solve the driving problem to completion. Waymo's autonomous platform is a frankenstein of various machine learning techniques, and much of it isn't glamorous, it's less contigent on big breakthroughs than it is on elbow grease. Google demoed as proof-of concept full autonomy in 2012, and much of what they've been doing in the 4 years between then and now is the tedious job of addressing and validating their system across the full spectrum of edge cases that must be dealt with if they ever hope to foist their safety critical software upon the public. It's not clear to me that Tesla's current development paradigm will ever be sufficient to completely take the human out of the loop. Tesla's approach is incremental, and I suspect they'll have to make some big changes if they wish to fully close the gap. Waymo has kept their eye on the prize from day 1.
  - asdfasdfa11112 10y ago
    Argument is that Tesla has more representative / real world data. Big public opinion perspective here too.
- agildehaus 10y ago
  It's important to note Google/Waymo's tech is limited to places that have been heavily manually mapped and test driven. They're basically creating a virtual track for their cars to follow, and presuming that changes in the real world can be detected and distributed to the entire fleet. Other companies seem to be taking a generalized AI approach. Their cars work everywhere, but how well? It'll be exciting to see which approach works best.
  - wstrange 10y ago
    This comes up on HN quite a bit, but I have never seen anything that suggests that Google doesn't also use a generalized AI approach. Having high res maps certainly helps, and if you have that data, then why not use it? It is no different than what people do. If you drive the same route all the time, you have some expectation of what lies ahead.
    - jayjay71 10y ago
      This information is not current, but I know that they used to detect traffic lights by looking for them in their maps. By that I mean they knew where the traffic light should be, then did the basic geometry to figure out where that is in their image, and then determined the color of the pixels to figure out what the light was saying. That absolutely would not work without the maps. I am willing to bet that their approach fundamentally depends on the maps. That's fine, it just means they can't work in areas that aren't already mapped (and you have to have one of the maps stored locally in the car, as those files are gigantic).
      - birdman3131 10y ago
        And in steps google street view. They have been planning this for years.
        jayjay71 10y ago
        Google Street View contains images, not point clouds. And based on my usage, it's updated roughly every year. This is not even close to sufficient for their self-driving cars.
- adevine 10y ago
  It makes me wonder, though, if Google is currently at a disadvantage because their fleet of self driving cars is teeny compared to what Tesla has and will soon have. That is, Tesla has tens of thousands of cars with Auto Pilot sensors on the road, and (afaik) they have access to all of that real-world data to train their algorithms. A big advantage I see in Tesla's strategy is that all of their cars are now shipping with full self-driving hardware. Even if that hardware isn't actually used to control the car, Tesla will have an order of magnitude more real-world data than anyone else.
  - Fricken 10y ago
    Lets not compare 1000 words of Reddit to 1000 words of Shakespeare. The quality of the data is very important.
  - datguacdoh 10y ago
    But can they actually send all that data back to their data centers? If they are really capturing that much data, they would need to send it back using consumer home internet connections which doesn't seem realistic. I don't think the fleet size is as large an advantage if you can't actually get 100% of the sensor data.
sllabres 10y ago
A software driving in a truck standing across the road, because confusing the truck is a road sign is nothing I would use autonomously. [1] And I would not call it autopilot, especially being in a product. [1] https://www.theguardian.com/technology/2016/jul/01/tesla-driver-killed-autopilot-self-driving-car-harry-potter https://www.theguardian.com/technology/2016/jul/01/tesla-dri...
jfoster 10y ago
I broadly agree that Tesla may take the lead. That said, I know they've announced autopilot being capable of handling fog and various weather conditions and have separately demoed their full autonomy, but I haven't seen anything combining the two.

Fricken 10y ago

Krafcik mentions in his presentation that their cars are down to .2 disengages every 1000 miles (which would more aptly be described as 1 disengagement every 5000 miles), and their cars are actively seeking out challenging situations, it's not just easy/random driving. This is incredible. They are leagues ahead of the nearest competition. Krafcik also mentions they are making the lions share of their progress now using their simulation engine, where they can model every conceivable variation of any bizarre/difficult encounter they have on real roads. In sim they accumulated a billion miles in 2016.

wstrange 10y ago
A lot of folks have been saying that Tesla will win, because they will gather more data from real conditions. This never made sense to me. You certainly need enough data, but how you interpret and process that data is far more important.
- datguacdoh 10y ago
  Also, how much of that data can you realistically send back to yourself? With Waymo they can pull the arrays from the cars nightly if they need to. Tesla, on the other hand, has to send the data back over a customers network connection which would be much more limiting. So Tesla might be getting tons of miles but if that's a billion not very detailed miles, it might not be as useful as a million incredibly detailed miles.
  - robszumski 10y ago
    I think Tesla pays for the network connection to all of its cars. This sends back remote telemetry and other self-driving data. It also serves up software updates and monitors the cars network for intrusions via a VPN.
    - datguacdoh 10y ago
      That network connection is using cellular, so at most it's an LTE connection but if I remember right, is actually just 3G. I can't believe they are pushing the kind of data people are talking about over that kind of connection.
- adevine 10y ago
  It makes sense because virtually all machine-learning algorithms work better, and can learn faster, if you have more data. Think of it this way: if Tesla wants to test a particular algorithm for a particular driving situation, they can "play it back" over an enormous amount of real-world situations. They will have tons more potential edge cases with which they can validate their algorithms.
  - flylib 10y ago
    the data they have is not detailed enough though has been the criticism
  - renesd 10y ago
    This used to be true. Modern machine learning needs way less data. The classic example is taking images and then transforming them in hundreds of ways (scaling, rotation, skew, etc) for training. Big data is no where near as much a competitive advantage as it was three years ago. It seems not everyone outside the field has noticed that though.
    - adevine 10y ago
      I wonder if this would be true in the case of self-driving car algorithms, though (which I know nothing about). It always seemed like the hard part about self-driving cars was the 0.1% edge cases where something out of the ordinary could result in a catastrophe if not handled correctly. Image classification seems like it would be very different, most importantly that 99.9% "correct" would be a great achievement, but for self-driving cars a .1% failure rate would be completely unacceptable.
      - elmar 10y ago
        We are 30 to 50 years away of a level 5 car.
    - eanzenberg 10y ago
      Lul thinking machine learning is only image classification
- stuckagain 10y ago
  The popular idea that Tesla will just keep stuffing matrices down the throat of their training pipeline until a self-driving inference model emerges from the other end doesn't make a lot of sense.
  - Fricken 10y ago
    Neural nets can take you most of the way there, but pattern recognition alone will not solve the driving problem to completion. Waymo's autonomous platform is a frankenstein of various machine learning techniques, and much of it isn't glamorous, it's less contigent on big breakthroughs than it is on elbow grease. Google demoed as proof-of concept full autonomy in 2012, and much of what they've been doing in the 4 years between then and now is the tedious job of addressing and validating their system across the full spectrum of edge cases that must be dealt with if they ever hope to foist their safety critical software upon the public. It's not clear to me that Tesla's current development paradigm will ever be sufficient to completely take the human out of the loop. Tesla's approach is incremental, and I suspect they'll have to make some big changes if they wish to fully close the gap. Waymo has kept their eye on the prize from day 1.
- asdfasdfa11112 10y ago
  Argument is that Tesla has more representative / real world data. Big public opinion perspective here too.
agildehaus 10y ago
It's important to note Google/Waymo's tech is limited to places that have been heavily manually mapped and test driven. They're basically creating a virtual track for their cars to follow, and presuming that changes in the real world can be detected and distributed to the entire fleet. Other companies seem to be taking a generalized AI approach. Their cars work everywhere, but how well? It'll be exciting to see which approach works best.
- wstrange 10y ago
  This comes up on HN quite a bit, but I have never seen anything that suggests that Google doesn't also use a generalized AI approach. Having high res maps certainly helps, and if you have that data, then why not use it? It is no different than what people do. If you drive the same route all the time, you have some expectation of what lies ahead.
  - jayjay71 10y ago
    This information is not current, but I know that they used to detect traffic lights by looking for them in their maps. By that I mean they knew where the traffic light should be, then did the basic geometry to figure out where that is in their image, and then determined the color of the pixels to figure out what the light was saying. That absolutely would not work without the maps. I am willing to bet that their approach fundamentally depends on the maps. That's fine, it just means they can't work in areas that aren't already mapped (and you have to have one of the maps stored locally in the car, as those files are gigantic).
    - birdman3131 10y ago
      And in steps google street view. They have been planning this for years.
      - jayjay71 10y ago
        Google Street View contains images, not point clouds. And based on my usage, it's updated roughly every year. This is not even close to sufficient for their self-driving cars.
adevine 10y ago
It makes me wonder, though, if Google is currently at a disadvantage because their fleet of self driving cars is teeny compared to what Tesla has and will soon have. That is, Tesla has tens of thousands of cars with Auto Pilot sensors on the road, and (afaik) they have access to all of that real-world data to train their algorithms. A big advantage I see in Tesla's strategy is that all of their cars are now shipping with full self-driving hardware. Even if that hardware isn't actually used to control the car, Tesla will have an order of magnitude more real-world data than anyone else.
- Fricken 10y ago
  Lets not compare 1000 words of Reddit to 1000 words of Shakespeare. The quality of the data is very important.
- datguacdoh 10y ago
  But can they actually send all that data back to their data centers? If they are really capturing that much data, they would need to send it back using consumer home internet connections which doesn't seem realistic. I don't think the fleet size is as large an advantage if you can't actually get 100% of the sensor data.

sllabres 10y ago

A software driving in a truck standing across the road, because confusing the truck is a road sign is nothing I would use autonomously. [1] And I would not call it autopilot, especially being in a product. [1] https://www.theguardian.com/technology/2016/jul/01/tesla-driver-killed-autopilot-self-driving-car-harry-potter https://www.theguardian.com/technology/2016/jul/01/tesla-dri...

jfoster 10y ago

I broadly agree that Tesla may take the lead. That said, I know they've announced autopilot being capable of handling fog and various weather conditions and have separately demoed their full autonomy, but I haven't seen anything combining the two.