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> humans can learn to drive in a matter of hours with only two vision sensors You use way more than two vision sensors in your head. But first, lets talk abou

> humans can learn to drive in a matter of hours with only two vision sensors You use way more than two vision sensors in your head. But first, lets talk about those vision sensors. They are mounted on an articulated scanning platform with numerous degrees of freedom. The dynamic range is astounding, and that dynamic range can be easily augmented with shaded lenses that can be mounted and removed as needed via an extremely reliable pick-and-place mechanism. The vision sensors are really equipped with dual-sensing technology for day- and night-mode. But, you also have extremely sensitive accelerometers in your ears. And force-sensing in your feet to measure applied acceleration/braking controls. And force-sensing in your hands to measure applied steering force, and measure road roughness. You also are sitting on a force sensor that serves as a back-up accelerometer. You have audio sensors that provide feedback about road surface, vehicle performance, and other agents sharing the road.

jvm___ 3y ago
Relevant short and long-term memory of every 3d object you've ever seen along a road-side and what the expected behaviour of those objects should be. Does a road-sign usually move by itself? No, maybe someone is carrying it. Is a ball coming from behind a car in a school-zone likely to be chased by a child...
- dbcurtis 3y ago
  > Is a ball coming from behind a car in a school-zone likely to be chased by a child. Yeah, that kind of agent prediction is a biggie. Agent prediction is something journalists don't talk about much, but is a huge research topic for AV practitioners. And since you make note of expected behavior of objects... my all-time favorite bug report out of Waymo (that made the public news) is a classic classifier/predictor/planner corner case leading to deadlock. Classifier identified a cyclist and labeled as "stopped" if cyclist has foot on the ground, and "moving" if both feet on pedals. Predictor would plot a trajectory for moving cyclist, planner decides action based on predicted agent behavior. So... Waymo vehicle and cyclist on two corners of 4-way stop. Cyclist is stopped, doing a perfectly balanced track stand with both feet clipped in. Feet on pedals so cyclist is labeled as moving agent... planner yields right-of-way to cyclist according to rules-of-the-road. Deadlock -- nobody moves.
  - jvm___ 3y ago
    Isn't that how Waymo killed a pedestrian/cyclist? It couldn't decide if she was a cyclist or pedestrian, as she was walking and pushing her bicycle, so it never braked. She was homeless so the shape of her bike wasn't a proper bike so it got confused.
    - magicalist 3y ago
      Uber, and it probably could have braked but the emergency braking was purposefully disabled. https://en.m.wikipedia.org/wiki/Death_of_Elaine_Herzberg https://en.m.wikipedia.org/wiki/Death_of_Elaine_Herzberg
      - dbcurtis 3y ago
        Yes, Uber, operating in Level 3, and safety driver was watching a movie on phone at the time. Very different circumstances. I don’t think this was a classifier defect. Not that I was ever a fan of Uber’s development program, but in this case I can’t fault the platform.

jvm___ 3y ago

Relevant short and long-term memory of every 3d object you've ever seen along a road-side and what the expected behaviour of those objects should be. Does a road-sign usually move by itself? No, maybe someone is carrying it. Is a ball coming from behind a car in a school-zone likely to be chased by a child...

dbcurtis 3y ago
> Is a ball coming from behind a car in a school-zone likely to be chased by a child. Yeah, that kind of agent prediction is a biggie. Agent prediction is something journalists don't talk about much, but is a huge research topic for AV practitioners. And since you make note of expected behavior of objects... my all-time favorite bug report out of Waymo (that made the public news) is a classic classifier/predictor/planner corner case leading to deadlock. Classifier identified a cyclist and labeled as "stopped" if cyclist has foot on the ground, and "moving" if both feet on pedals. Predictor would plot a trajectory for moving cyclist, planner decides action based on predicted agent behavior. So... Waymo vehicle and cyclist on two corners of 4-way stop. Cyclist is stopped, doing a perfectly balanced track stand with both feet clipped in. Feet on pedals so cyclist is labeled as moving agent... planner yields right-of-way to cyclist according to rules-of-the-road. Deadlock -- nobody moves.
- jvm___ 3y ago
  Isn't that how Waymo killed a pedestrian/cyclist? It couldn't decide if she was a cyclist or pedestrian, as she was walking and pushing her bicycle, so it never braked. She was homeless so the shape of her bike wasn't a proper bike so it got confused.
  - magicalist 3y ago
    Uber, and it probably could have braked but the emergency braking was purposefully disabled. https://en.m.wikipedia.org/wiki/Death_of_Elaine_Herzberg https://en.m.wikipedia.org/wiki/Death_of_Elaine_Herzberg
    - dbcurtis 3y ago
      Yes, Uber, operating in Level 3, and safety driver was watching a movie on phone at the time. Very different circumstances. I don’t think this was a classifier defect. Not that I was ever a fan of Uber’s development program, but in this case I can’t fault the platform.