3 ms·

Useful but not at all required. Camera + radar is sufficient for driving, and camera+ USS is fine for parking. Radar is just cheaper than the number of camera

Useful but not at all required. Camera + radar is sufficient for driving, and camera+ USS is fine for parking. Radar is just cheaper than the number of cameras and compute, it's also not really a strict requirement. Look at how the current cars fuck up, it's mostly navigation, context understanding, and tight manoeuvres. Lidar gives you very little in these areas

UltraSane 8mo ago
All of the actually WORKING self driving systems use LIDAR. This is not a coincidence.
- small_model 8mo ago
  Like Waymo? (https://dmnews.co.uk/waymo-robotaxi-spotted-unable-to-cross-puddle-in-los-angeles/ https://dmnews.co.uk/waymo-robotaxi-spotted-unable-to-cross-...) 17 years after betting the farm on LIDAR the solution fails to navigate a puddle. Sorry but they bet on the wrong technology, Tesla has overtaken them with multi camera and NN solution.
  - UltraSane 8mo ago
    Your conclusion from a single incident is a bad inference. One vehicle getting confused by a puddle (likely a sensor fusion edge case or mapping artifact, not a fundamental LIDAR failure) doesn't indict the technology. Tesla's cameras have produces vastly more failures. Waymo has driven tens of millions of autonomous miles with a serious injury/fatality rate dramatically lower than human drivers. The actual data shows the technology works. Tesla FSD still requires active driver supervision and is not legally or technically a robotaxi system. Comparing them as if they're at parity is wrong. LIDAR gives direct metric depth with no inference required. Camera-only systems must infer depth from 2D images using neural networks, which introduces failure modes LIDAR doesn't have. Radar is very valuable when LIDAR and cameras give ambiguous data. What metrics has Telsa overtaken Waymo? Deployed robotaxi revenue miles? No. Disengagement rates? No published comparable data. Safety per mile in driverless operation? No.
    - small_model 8mo ago
      A Tesla wouldn't stop for a puddle. Also its not locked to a small geofenced area (people have driven coast to coast without a single intervention on FSD including parking spot to parking spot) when I can buy a Waymo vehicle that does this then Waymo would have caught up with Tesla.
      - jamespo 8mo ago
        Wow, so it can cope with driving on the highway. That's the easy part.
      - UltraSane 8mo ago
        Your puddle example is utterly irrelevant. Tesla's are notorious for phantom breaking. Robotaxis are very much locked to tiny geofenced areas. Some even shaped like a penis because Musk is such a child. "people have driven coast to coast without a single intervention on FSD including parking spot to parking spot" I find this claim very dubious. Prove it. Teslas never drive empty for a very good reason.
        small_model 8mo ago
        Err they have lots of Model Ys in Austin as Robotaxis right now with no drivers. I guess this is also 'dubious'. Look it's clear you have a huge bias I would urge you to read up on https://grokipedia.com/page/List_of_fallacies https://grokipedia.com/page/List_of_fallacies otherwise your emotional responses will blind you to reality.
        moooo99 8mo ago
        > Look it's clear you have a huge bias I would urge you to read up on https://grokipedia.com/page/List_of_fallacies https://grokipedia.com/page/List_of_fallacies otherwise your emotional responses will blind you to reality. Writing this and linking to fake Wikipedia is actually hilarious.
        UltraSane 8mo ago
  - cheema33 8mo ago
    > Tesla has overtaken them with multi camera and NN solution. Let me guess, you heard this from Elon?
- servo_sausage 8mo ago
  I work with programs approaching L3+ from L2, with the requirement that the system works for 99% of roads (not tesla before people start fixating on that). We find that the cases where lidar really helps are in gathering training data, parking, and if focused enough some long distance precision. None of these have been instrumental in a final product; personally I suspect that many of the cars including lidar use it for data collection and edge cases more than as part of the driving perception model.
  - heisenbit 8mo ago
    Accidents are not normal driving situations but edge cases.
    - servo_sausage 8mo ago
      Sort of; accidents are the absolute core of the product. They are rare, but they are the focus of the design. By edge cases I mean scenarios like the lights going out in an underground garage; low vision due to colourful smoke or dust, or things like optical illusions or occlusion that a human would just need to remember. Lidar can help, but not really enough to be worth it.
      - UltraSane 8mo ago
        Lidar is by far the most accurate source of range data. You need to explain why Waymo and Zoox use lidar in direct contradiction to what you claim.
        servo_sausage 8mo ago
        Urban operating domain combined with legacy approaches. If I was designing a robotaxi 10 years ago I would use lidar, designing consumer vehicles for near future L3 it's no longer the best use of resources. I prefer more compute and cameras for the money. Our current issues are now scene understanding and navigation; followed by parking. We get very little value from LIDAR in the driving cases, so much so that we don't even use it for active nav even on cars that have it. Only for training and parking.

UltraSane 8mo ago

All of the actually WORKING self driving systems use LIDAR. This is not a coincidence.

small_model 8mo ago
Like Waymo? (https://dmnews.co.uk/waymo-robotaxi-spotted-unable-to-cross-puddle-in-los-angeles/ https://dmnews.co.uk/waymo-robotaxi-spotted-unable-to-cross-...) 17 years after betting the farm on LIDAR the solution fails to navigate a puddle. Sorry but they bet on the wrong technology, Tesla has overtaken them with multi camera and NN solution.
- UltraSane 8mo ago
  Your conclusion from a single incident is a bad inference. One vehicle getting confused by a puddle (likely a sensor fusion edge case or mapping artifact, not a fundamental LIDAR failure) doesn't indict the technology. Tesla's cameras have produces vastly more failures. Waymo has driven tens of millions of autonomous miles with a serious injury/fatality rate dramatically lower than human drivers. The actual data shows the technology works. Tesla FSD still requires active driver supervision and is not legally or technically a robotaxi system. Comparing them as if they're at parity is wrong. LIDAR gives direct metric depth with no inference required. Camera-only systems must infer depth from 2D images using neural networks, which introduces failure modes LIDAR doesn't have. Radar is very valuable when LIDAR and cameras give ambiguous data. What metrics has Telsa overtaken Waymo? Deployed robotaxi revenue miles? No. Disengagement rates? No published comparable data. Safety per mile in driverless operation? No.
  - small_model 8mo ago
    A Tesla wouldn't stop for a puddle. Also its not locked to a small geofenced area (people have driven coast to coast without a single intervention on FSD including parking spot to parking spot) when I can buy a Waymo vehicle that does this then Waymo would have caught up with Tesla.
    - jamespo 8mo ago
      Wow, so it can cope with driving on the highway. That's the easy part.
    - UltraSane 8mo ago
      Your puddle example is utterly irrelevant. Tesla's are notorious for phantom breaking. Robotaxis are very much locked to tiny geofenced areas. Some even shaped like a penis because Musk is such a child. "people have driven coast to coast without a single intervention on FSD including parking spot to parking spot" I find this claim very dubious. Prove it. Teslas never drive empty for a very good reason.
      - small_model 8mo ago
        Err they have lots of Model Ys in Austin as Robotaxis right now with no drivers. I guess this is also 'dubious'. Look it's clear you have a huge bias I would urge you to read up on https://grokipedia.com/page/List_of_fallacies https://grokipedia.com/page/List_of_fallacies otherwise your emotional responses will blind you to reality.
        moooo99 8mo ago
        > Look it's clear you have a huge bias I would urge you to read up on https://grokipedia.com/page/List_of_fallacies https://grokipedia.com/page/List_of_fallacies otherwise your emotional responses will blind you to reality. Writing this and linking to fake Wikipedia is actually hilarious.
        UltraSane 8mo ago
- cheema33 8mo ago
  > Tesla has overtaken them with multi camera and NN solution. Let me guess, you heard this from Elon?
servo_sausage 8mo ago
I work with programs approaching L3+ from L2, with the requirement that the system works for 99% of roads (not tesla before people start fixating on that). We find that the cases where lidar really helps are in gathering training data, parking, and if focused enough some long distance precision. None of these have been instrumental in a final product; personally I suspect that many of the cars including lidar use it for data collection and edge cases more than as part of the driving perception model.
- heisenbit 8mo ago
  Accidents are not normal driving situations but edge cases.
  - servo_sausage 8mo ago
    Sort of; accidents are the absolute core of the product. They are rare, but they are the focus of the design. By edge cases I mean scenarios like the lights going out in an underground garage; low vision due to colourful smoke or dust, or things like optical illusions or occlusion that a human would just need to remember. Lidar can help, but not really enough to be worth it.
    - UltraSane 8mo ago
      Lidar is by far the most accurate source of range data. You need to explain why Waymo and Zoox use lidar in direct contradiction to what you claim.
      - servo_sausage 8mo ago
        Urban operating domain combined with legacy approaches. If I was designing a robotaxi 10 years ago I would use lidar, designing consumer vehicles for near future L3 it's no longer the best use of resources. I prefer more compute and cameras for the money. Our current issues are now scene understanding and navigation; followed by parking. We get very little value from LIDAR in the driving cases, so much so that we don't even use it for active nav even on cars that have it. Only for training and parking.