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The general consensus among my AV friends (who work at a bunch of different companies) is that their AV driving stack is really good, but obviously not perfect.

The general consensus among my AV friends (who work at a bunch of different companies) is that their AV driving stack is really good, but obviously not perfect. I have no idea about their business model and how COVID affects that, though.

Fricken 6y ago
Relative to competitors Zoox's automous OS is doing quite well and doesn't get enough respect. Relative to the objective everybody is fucked.
- xiaolingxiao 6y ago
  Could you provide more context on the first part.
  - Fricken 6y ago
    They've been keeping abreast. The co-founder, Tim Kentley Klay was somehow able to get Jesse Levinson on board, and Jesse Levinson had no problem getting infinite street cred on board. So they were able to attract a lot of key, original robotics talent before the hype got out of control. For a long time though, they were low on funds, so they did lots of closed course testing, and it wasn't until they closed a large funding round that Zoox began on public roads, and they performed quite well right out of the starting gate. Now Zoox and it's competitors are lost in an endless wasteland of testing, development, and validation. It's futile to attempt to do a comprehensive analysis between the different players, they all have their quirks, but Zoox has built all the critical infrastructure needed to do full scale testing, and they're eyeballs deep in it like everyone else. However, Zoox has stormy waters ahead financially. They need another $2 billion to stay abreast in this never ending race. It's getting harder to visualize scenarios where that happens. What nobody can do well enough to build a competitive and scalable robotaxi service is prediction in multi-agent scenarios. The AI for that just doesn't exist.
    - matdehaast 6y ago
      Thanks for the info. Can you clarify what predictions in multi agent scenarios are?
      - ragebol 6y ago
        I think was is meant is: in case you have a few people and AVs in an interaction, predict who's going to do what in order to best anticipate the overall outcome. Not sure humans can do that outside of conversation and norms and rules.
      - Fricken 6y ago
        Imagine an uncontrolled intersection. The Robotaxi is approaching from one direction. In the opposite direction is a cyclist who intends to turn left across the Robotaxi. There is also a pedestrian that may or may not cross the street, and another vehicle about to cross in front of the Robotaxi from the other direction. There are a huge number of ways this scenario can play out, and any decision made by one agent can affect the behaviors of all the others, compounding it's complexity. Humans can game out these situations intuitively, but current AI cannot read deep enough into the matrix to deal with these situations quickly and reliably.
        BubRoss 6y ago
        There are rules to that situation. You can start there, let other cars go ahead of you if they break the rules, go slow and not hit anything. It isn't as if self driving cars can't look to the side or stop if something changes. Beyond that Jim Keller would say that not getting hit by something else is a matter of ballistics.
        TheSmiddy 6y ago
        In Australia there are no uncontrolled intersections (that I am aware of). Every single junction clearly marks who must give way and we don't have any 4-way stops, instead using roundabouts in these situations. It's possible that for self driving to work road systems will have to be more formalised to remove the ambiguous situations you've described. I can't imagine it working well in China or Indonesia where traffic flows much more like water in a stream and lanes are merely just suggestions.

Fricken 6y ago

Relative to competitors Zoox's automous OS is doing quite well and doesn't get enough respect. Relative to the objective everybody is fucked.

xiaolingxiao 6y ago
Could you provide more context on the first part.
- Fricken 6y ago
  They've been keeping abreast. The co-founder, Tim Kentley Klay was somehow able to get Jesse Levinson on board, and Jesse Levinson had no problem getting infinite street cred on board. So they were able to attract a lot of key, original robotics talent before the hype got out of control. For a long time though, they were low on funds, so they did lots of closed course testing, and it wasn't until they closed a large funding round that Zoox began on public roads, and they performed quite well right out of the starting gate. Now Zoox and it's competitors are lost in an endless wasteland of testing, development, and validation. It's futile to attempt to do a comprehensive analysis between the different players, they all have their quirks, but Zoox has built all the critical infrastructure needed to do full scale testing, and they're eyeballs deep in it like everyone else. However, Zoox has stormy waters ahead financially. They need another $2 billion to stay abreast in this never ending race. It's getting harder to visualize scenarios where that happens. What nobody can do well enough to build a competitive and scalable robotaxi service is prediction in multi-agent scenarios. The AI for that just doesn't exist.
  - matdehaast 6y ago
    Thanks for the info. Can you clarify what predictions in multi agent scenarios are?
    - ragebol 6y ago
      I think was is meant is: in case you have a few people and AVs in an interaction, predict who's going to do what in order to best anticipate the overall outcome. Not sure humans can do that outside of conversation and norms and rules.
    - Fricken 6y ago
      Imagine an uncontrolled intersection. The Robotaxi is approaching from one direction. In the opposite direction is a cyclist who intends to turn left across the Robotaxi. There is also a pedestrian that may or may not cross the street, and another vehicle about to cross in front of the Robotaxi from the other direction. There are a huge number of ways this scenario can play out, and any decision made by one agent can affect the behaviors of all the others, compounding it's complexity. Humans can game out these situations intuitively, but current AI cannot read deep enough into the matrix to deal with these situations quickly and reliably.
      - BubRoss 6y ago
        There are rules to that situation. You can start there, let other cars go ahead of you if they break the rules, go slow and not hit anything. It isn't as if self driving cars can't look to the side or stop if something changes. Beyond that Jim Keller would say that not getting hit by something else is a matter of ballistics.
      - TheSmiddy 6y ago
        In Australia there are no uncontrolled intersections (that I am aware of). Every single junction clearly marks who must give way and we don't have any 4-way stops, instead using roundabouts in these situations. It's possible that for self driving to work road systems will have to be more formalised to remove the ambiguous situations you've described. I can't imagine it working well in China or Indonesia where traffic flows much more like water in a stream and lanes are merely just suggestions.