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Waymo operates on guardrails, with a lot more human-in-the-loop (remotely) help than most people seem aware of. Tesla's already have similar capabilities, in a

by Rover222 11mo ago

Waymo operates on guardrails, with a lot more human-in-the-loop (remotely) help than most people seem aware of. Tesla's already have similar capabilities, in a much wider range of roads, in vehicles that cost 80% less to manufacture. They're both achieving impressive results. But if you read beyond headlines, Tesla is setup for such more more success than Waymo in the next 1-2 years.

ra7 11mo ago
Tesla literally has a human in the driver seat for each and every mile. Their robotaxi which operates on geofenced “guardrails” has a human in the driver seat or passenger seat depending on area of its operation, and also has active remote supervision. That’s direct supervision 100% of the time. It is in no way similar in capability to Waymo. We’ve been hearing Tesla will “surpass Waymo in the next 1-2 years” from the past 8 years, yet they are nowhere close. It’s always future tense with Tesla and never about the current state.
JumpCrisscross 11mo ago
> Tesla is setup for such more more success than Waymo in the next 1-2 years Iff cameras only works. With threshold for "works" beig set by Waymo, since a Robotaxi that's would have been acceptable per se may not be if it's statistically less safe compared to an existing solution. Waymo also sets the timeline. If cameras only would work, but Waymo scales before it does, Tesla may be forced by regulators to integrate radars and lidars. This nukes their cost advantage, at least in part, though Tesla maintains its manufacturing lead and vertical integration.) Tesla has a good hand. But Rivian's play makes sense. If cameras only fails, they win on licensing and a temporary monopoly. If cameras only work, they are a less-threatening partner for other car companies than Waymo.
- simondotau 11mo ago
  In the increasingly rare instances where Tesla's solution is making mistakes, it's pretty much never to do with a failure of spatial awareness (sensing) but rather a failure of path planning (decision-making). The only thing LIDAR can do sense depth, and if it turns out sensing depth using cameras is a solved problem, adding LIDAR doesn't help. It can't read road signs. It can't read road lines. It can't tell if a traffic light is red or green. And it certainly doesn't improve predictions of human drivers.
  - dzhiurgis 11mo ago
    Which begs me the question why Tesla took so long to get here? It's only since v12 it starting to look bearable for supervised use. The only answer I see is their goal to create global model that works in every part of the world vs single city which is vastly more difficult. After all most drivers really only know how to drive well in their own town and make a lot of mistakes when driving somewhere else.
    - simondotau 11mo ago
      Path planning (decision-making) is by far the most complicated part of self-driving. Waymo vehicles were making plenty of comically stupid mistakes early on, because having sufficient spatial accuracy was never the truly hard part.
    - Rover222 11mo ago
      It was only about 2 years ago that they switched from hard coded logic to machine learning (video in, car control out), and this was the beginning of their final path they are committed to now. (building out manufacturing for Cybercab while still finalizing the FSD software is a pretty insane risk that no other company would take)
      - dzhiurgis 11mo ago
        That’s the switch for controls, the machine vision was nn from the start.
  - KeplerBoy 11mo ago
    Sensing depth is pretty important though. Especially in scenarios where vision fails, radar for example works perfectly fine in the thickest of fog.
    - simondotau 11mo ago
      In "scenarios where vision fails" the car should not be driving. Period. End of story. It doesn't matter how good radar is in fog, because radar alone is not enough.

ra7 11mo ago

Tesla literally has a human in the driver seat for each and every mile. Their robotaxi which operates on geofenced “guardrails” has a human in the driver seat or passenger seat depending on area of its operation, and also has active remote supervision. That’s direct supervision 100% of the time. It is in no way similar in capability to Waymo. We’ve been hearing Tesla will “surpass Waymo in the next 1-2 years” from the past 8 years, yet they are nowhere close. It’s always future tense with Tesla and never about the current state.

JumpCrisscross 11mo ago

> Tesla is setup for such more more success than Waymo in the next 1-2 years Iff cameras only works. With threshold for "works" beig set by Waymo, since a Robotaxi that's would have been acceptable per se may not be if it's statistically less safe compared to an existing solution. Waymo also sets the timeline. If cameras only would work, but Waymo scales before it does, Tesla may be forced by regulators to integrate radars and lidars. This nukes their cost advantage, at least in part, though Tesla maintains its manufacturing lead and vertical integration.) Tesla has a good hand. But Rivian's play makes sense. If cameras only fails, they win on licensing and a temporary monopoly. If cameras only work, they are a less-threatening partner for other car companies than Waymo.

simondotau 11mo ago
In the increasingly rare instances where Tesla's solution is making mistakes, it's pretty much never to do with a failure of spatial awareness (sensing) but rather a failure of path planning (decision-making). The only thing LIDAR can do sense depth, and if it turns out sensing depth using cameras is a solved problem, adding LIDAR doesn't help. It can't read road signs. It can't read road lines. It can't tell if a traffic light is red or green. And it certainly doesn't improve predictions of human drivers.
- dzhiurgis 11mo ago
  Which begs me the question why Tesla took so long to get here? It's only since v12 it starting to look bearable for supervised use. The only answer I see is their goal to create global model that works in every part of the world vs single city which is vastly more difficult. After all most drivers really only know how to drive well in their own town and make a lot of mistakes when driving somewhere else.
  - simondotau 11mo ago
    Path planning (decision-making) is by far the most complicated part of self-driving. Waymo vehicles were making plenty of comically stupid mistakes early on, because having sufficient spatial accuracy was never the truly hard part.
  - Rover222 11mo ago
    It was only about 2 years ago that they switched from hard coded logic to machine learning (video in, car control out), and this was the beginning of their final path they are committed to now. (building out manufacturing for Cybercab while still finalizing the FSD software is a pretty insane risk that no other company would take)
    - dzhiurgis 11mo ago
      That’s the switch for controls, the machine vision was nn from the start.
- KeplerBoy 11mo ago
  Sensing depth is pretty important though. Especially in scenarios where vision fails, radar for example works perfectly fine in the thickest of fog.
  - simondotau 11mo ago
    In "scenarios where vision fails" the car should not be driving. Period. End of story. It doesn't matter how good radar is in fog, because radar alone is not enough.